1. Michalski, R., Kazienko, P.: Social Network Analysis in Organizational Structures Evaluation. Alhajj, R., Rokne, J. (eds.) Encyclopedia of Social Network Analysis and Mining, Living reference work, pp. 1-13. Springer (2017)
2. Michalski, R., Kazienko, P.: Maximizing Social Influence in Real-World Networks - the State of the Art and Current Challenges. Król, D., Fay, D., Gabryś, B. (eds.) Propagation Phenomena in Real World Networks, Intelligent Systems Reference Library, Vol. 85, pp. 329-359. Springer (2015)
3. Michalski, R., Kazienko, P.: Social Network Analysis in Organizational Structures Evaluation. Alhajj, R., Rokne, J. (eds.) Encyclopedia of Social Network Analysis and Mining, vol. 3, pp. 1832-1844. Springer (2014)
4. Palus, S., Kazienko, P., Michalski, R.: Evaluation of Corporate Structure Based on Social Network Analysis. Cakir, A., De Pablos, P.O. (eds.) Social Development and High Technology Industries: Strategies and Applications, pp. 58-69. IGI-Global (2012)

1. Weskida, M., Michalski, R.: Finding Inuentials in Social Networks using Evolutionary Algorithm. Journal of Computational Science (JCR-listed journal), Vol. 31, pp. 77-85 (2019)
2. Jankowski, J., Waniek, M., Ali Alshamsi, A., Bródka, P., Michalski, R.: Strategic Distribution of Seeds to Support Diffusion in Complex Networks. PLoS ONE (JCR-listed journal), 13(10): e0205130 (2018)
3. Kulisiewicz, M., Kazienko, P., Szymański, B., Michalski, R.: Entropy Measures of Human Communication Dynamics. Scientific Reports (JCR-listed journal), 8, article number 15697, Nature Publishing Group (2018)
4. Jankowski, J., Szymański, B., Kazienko, P., Michalski, R., Bródka, P.: Probing Limits of Information Spread with Sequential Seeding. Scientific Reports (JCR-listed journal), 8, article number 13996, Nature Publishing Group (2018)
5. Jankowski, J., Michalski, R., Bródka, P.: A multilayer network dataset of interaction and influence spreading in a virtual world. Scientific Data (JCR-listed journal), 4, article number 170144, Nature Publishing Group (2017)
6. Jankowski, J., Bródka, P., Kazienko, P., Szymański, B., Michalski, R., Kajdanowicz, T.: Balancing Speed and Coverage by Sequential Seeding in Complex Networks. Scientific Reports (JCR-listed journal), 7, article number 891, Nature Publishing Group (2017)
7. Ethier, JF, Curcin, V., McGilchrist, M., Lim Choi Keung, S., Zhao, L., Andreasson, A., Bródka, P., Michalski, R., Arvanitis T., Mastellos, N., Burgun, A., Delaney B.: eSource for clinical trials: Implementation and evaluation of a standards-based approach in a real world trial, International Journal of Medical Informatics (JCR-listed journal), Volume 106, pp. 17-24, Elsevier (2017)
8. Kajdanowicz, T., Michalski, R., Musiał, K., Kazienko, P.: Learning in Unlabelled Networks - An Active Learning and Inference Approach. AI Communications (JCR-listed journal), Vol. 29, No. 1, pp. 123-148, IOS Press (2016) [abstract]
9. Jankowski. J., Michalski, R., Bródka, P., Kazienko, P., Utz, S.: Knowledge Acquisition from Social Platforms Based on Network Distributions Fitting. Computers in Human Behavior (JCR-listed journal), Vol. 51, Part B, pp. 685–693, Elsevier (2015)
10. Różewski, P., Jankowski. J., Bródka, P., Michalski, R.: Knowledge Workers' Collaborative Learning Behavior Modeling in an Organizational Social Network. Computers in Human Behavior (JCR-listed journal), Vol. 51, Part B, pp. 1248–1260, Elsevier (2015)
11. Michalski, R., Kajdanowicz, T., Bródka, P., Kazienko, P.: Seed Selection for Spread of Influence in Social Networks: Temporal vs. Static Approach. New Generation Computing (JCR-listed journal), Vol. 32, Issue 3-4, pp. 213-235. Ohmsha-Japan and Springer (2014) [abstract]

1. Michalski, R., Szymański, B.K., Kazienko, P., Lebiere, C., Lizardo, O., Kulisiewicz, M.: Social Networks through the Prism of Cognition. arXiv preprint 1806.04658 (2018)
2. Nurek, M., Michalski, R.: Combining Machine Learning and Social Network Analysis to Reveal the Organizational Structures. arXiv preprint 1906.09576 (2019)

1. Jankowski, J., Waniek, M., Alshamsi, A., Bródka, P., Michalski, R.: Strategic distribution of seeds to support diffusion in complex networks. NetSci 2019, The 2019 International School and Conference on Network Science, Burlington, VT, United States (2019)
2. Jankowski, J., Szymański, B., Kazienko, P., Michalski, R., Bródka, P.: Probing Limits of Information Spread with Sequential Seeding. NetSci-X 2019, The 2019 International School and Conference on Network Science, Santiago, Chile (2019)
3. Kulisiewicz, M., Kazienko, P., Szymański, B., Michalski, R.: Entropy of Evolving Temporal Social Networks. Poster at IC2S2 2018, 4th Annual International Conference on Computational Social Science, Northwestern University, Evanston, IL, United States (2018)
4. Jankowski, J., Kazienko, P., Szymański, B., Michalski, R., Bródka, P.: Studying Limits of Information Spread with Sequential Seeding. Poster at IC2S2 2018, 4th Annual International Conference on Computational Social Science, Northwestern University, Evanston, IL, United States (2018)
5. Jankowski, J., Bródka, P., Michalski, R., Kazienko, P., Szymański, B., Kajdanowicz, T.: Sequential Seeding as a way of Reaching the Boundaries of Information Spread. Transversal Problems in Complexity, Université de Cergy-Pontoise, Cergy, France (2018)
6. Michalski, R., Zychal, B.: Blockchain as a Complex Network - the Analysis of Trends of Bitcoin Blockchain. BlockNet 2018, Satellite Symposium at The 2018 International School and Conference on Network Science, Paris, France (2018)
7. Michalski, R., Szymański, B., Kazienko, P., Lebiere, C., Lizardo, O., Kulisiewicz, M.: CogSNet: Cognition-driven Social Network. NetSci 2018, The 2018 International School and Conference on Network Science, Paris, France (2018)
8. Jankowski, J., Kazienko, P., Szymański, B., Michalski, R., Bródka, P.: Sequential Seeding for Exploring Boundaries of Current Limits on Information Spreading Coverage. NetSci 2018, The 2018 International School and Conference on Network Science, Paris, France (2018)
9. Kulisiewicz, M., Kazienko, P., Szymański, B., Michalski, R., Bródka, P.: Phenomena of Entropy in Temporal Network. Poster at NetSci 2018, The 2018 International School and Conference on Network Science, Paris, France (2018)
10. Jankowski, J., Bródka, P., Kazienko, P., Szymański, B., Michalski, R., Kajdanowicz, T.: Towards Reasonable Budget Allocation for Spreading Processes: Sequential Seeding. NetSci-X 2018, The 2018 International School and Conference on Network Science, Hangzhou, China (2018)
11. Jankowski, J., Bródka, P., Michalski, R., Kazienko, P.: Seeds Buffering for Information Spreading Processes. SocInfo 2017, 9th International Conference on Social Informatics, Lecture Notes in Computer Science LNCS, vol. 10539, pp. 628-641, Springer, Berlin Heidelberg (2017)
12. Jankowski, J., Michalski, R.: Increasing Coverage of Information Spreading in Social Networks with Supporting Seeding. DMBD 2017, The Second International Conference on Data Mining and Big Data, Lecture Notes in Computer Science LNCS, vol 10387, pp. 209-218, Springer, Berlin Heidelberg (2017)
13. Jankowski, J., Michalski, R., Bródka, P., Karczmarczyk, A.: Increasing coverage of information diffusion processes by reducing the number of initial seeds, MSDNS 2017 Workshop at ASONAM 2017, The 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. IEEE Computer Society, pp. 713-720 (2017)
14. Jankowski, J., Bródka, P., Kazienko, P., Szymański, B., Kajdanowicz, T., Michalski, R.: Sequential Seeding in Complex Networks - Trading Speed for Coverage. Poster at NetSci-X 2017, The 2017 International School and Conference on Network Science (2017)
15. Michalski, R., Weskida, M.: Using Evolutionary Algorithm and GPGPU for Finding Influential Nodes in Social Networks. Poster at NetSci-X 2017, The 2017 International School and Conference on Network Science (2017)
16. Weskida, M., Michalski, R.: Evolutionary Algorithm for Seed Selection in Social Influence Process. SNAA 2016 Workshop at ASONAM 2016, The 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. IEEE Computer Society, pp. 1189-1196 (2016)
17. Michalski, R., Kazienko, P., Kulisiewicz M.: Core Nodes as the Influencers in Temporal Social Networks. Poster at ICCSS 2016 - 2016 International Conference on Computational Social Science. Evanston, IL, USA (2016)
18. Michalski, R., Kazienko, P., Kulisiewicz M.: Powerful by Presence: the Role of Core Nodes in the Social Influence Process. Lightning talk at NetSci 2016 - International School and Conference on Network Science. Seoul, Korea (2016)
19. Jankowski, J., Bródka, P., Kajdanowicz, T., Szymański, B., Kazienko, P., Michalski, R.: Sequential Seeding in Social Networks with the Dynamic Recomputation of Network Measures. Lightning talk at NetSci 2016 - International School and Conference on Network Science. Seoul, Korea (2016)
20. Jankowski, J., Kajdanowicz, T., Bródka, P., Michalski, R., Kazienko, P.: Sequential Seeding in Social Networks. Poster at NetSci-X 2016 - International School and Conference on Network Science. Wrocław, Poland (2016)
21. Kulisiewicz, M., Lawyer, G., Michalski, R.: How to Find the most Infectious Nodes in Temporal Setting?. Talk at NetSci-X 2016 - International School and Conference on Network Science. Wrocław, Poland (2016)
22. Tuligłowicz, W., Jankowski, J., Michalski, R., Kazienko, P.: Spread of Negative User Behaviours in Social Networks. Talk at NetSci-X 2016 - International School and Conference on Network Science. Wrocław, Poland (2016)
23. Jankowski, J., Michalski, R., Kazienko, P., Bródka, P., Utz, S.: Adaptive Survey Design Using Structural Characteristics of the Social Network. SocInfo 2015 - The 7th International Conference on Social Informatics. Lecture Notes in Computer Science LNCS, vol. 9471, pp. 153-163, Springer, Berlin Heidelberg (2015)
24. Kulisiewicz, M., Lawyer, G., Michalski, R.: Predicting the Spreading Power of Infectious Nodes in Temporal Networks. Talk at CCS'15 Satellite Workshop: Contagion'15 - Modeling of Disease Contagion Processes (2015)
25. Michalski, R., Kazienko, P.: The Spreading Power of Core Nodes in Temporal Social Networks. Talk at CCS'15 Satellite Workshop: Computational Social Science (2015)
26. Michalski, R.: Linear Threshold Model in Temporal Networks - Seed Selection for Social Influence. ASONAM 2015, The 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. IEEE Computer Society, pp. 922-923 (2015)
27. Michalski, R., Kajdanowicz, T., Bródka, P., Kazienko, P.: Spread of Influence in Temporal Networks. Poster at International Conference on Computational Social Science ICCSS 2015
28. Bródka, P., Kazienko, P., Michalski, R.: Group Extraction in Multi-layered Social Network, NetSci 2015
29. Michalski, R., Jankowski, J., Bródka, P., Kazienko, P.: The Same Network - Different Communities? The Multidimensional Study of Groups in the Cyberspace. ASONAM 2014, The 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. IEEE Computer Society, pp. 864-869 (2014)
30. Bródka, P., Kazienko, P., Andreasson, A., Frączkowski, K., Misiaszek, A., Saganowski, S., Zhao, L., Curcin, V., Arvanitis, T.N., Delaney, B.C., Michalski, R.: Remote Patient Health Condition Monitoring for Clinical Research. Talk at WONCA 2014 (2014)
31. Michalski, R., Kazienko, P., Kajdanowicz, T., Bródka, P.: Data-driven Seed Selection for Spread of Influence in Temporal Social Networks. Workshop on Sociophysics at SigmaPhi 2014 - The International Conference on Statistical Physics 2014, Kaniadakis G., Scarfone A.M. (eds.), p. 75. (2014)
32. Michalski, R., Kajdanowicz, T., Bródka, P., Kazienko, P.: Seed Selection in Social Networks - Temporal Approach Benefits. Satellite Symposium on Temporal Networks, Human Dynamics and Social Physics (TnetSphys'14) at NetSci 2014
33. Kajdanowicz, T., Michalski, R., Musiał, K., Kazienko, P.: Active Learning and Inference for Classification in Networks. Poster at NetSci 2014
34. Michalski, R., Kazienko, P., Jankowski, J.: Convince a Dozen More and Succeed - The Influence in Multi-layered Social Networks. The Second Workshop on Complex Networks and their Applications at SITIS 2013 - The 9th International Conference on Signal Image Technology & Internet based Systems, December 2-5 2013, Kyoto, Japan, IEEE Computer Society, pp. 499-505 (2013) [abstract]
35. Michalski, R., Jankowski, J., Bródka, P., Kazienko, P.: How the Network Dynamics Influences the Diffusion of Innovations. Poster at Workshop on Temporal and Dynamic Networks: From Data to Models - NetSci 2013
36. Bródka, P., Kazienko, P., Saganowski, S., Michalski, R.: Quantifying Multi-layered Complex Networks. Satellite Symposium on Multiple Network Modeling, Analysis and Mining at NetSci 2013
37. Michalski, R., Kazienko, P., Jankowski, J.: Analysing Viral Campaigns in Social Networks by Means of Branching Processes. Poster at NetSci 2013
38. Kajdanowicz, T., Michalski, R., Musiał-Gabryś, K., Kazienko, P.: Active Learning and Inference Method for Within Network Classification. ASONAM 2013, The 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. IEEE Computer Society, pp. 1299-1306 (2013)
39. Jankowski, J., Michalski, R., Kazienko, P.: Compensatory Seeding in Networks with Varying Availability of Nodes. ASONAM 2013, The 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. IEEE Computer Society, pp. 1242-1249 (2013) [abstract]
40. Kazienko, P., Kajdanowicz, T., Michalski, R., Bródka, P.: From Data to Human Behaviour. SOCIETY 2013 - International Conference on Social Intelligence and Technology, IEEE Computer Society, pp. 100-108 (2013)
41. Jankowski, J., Kozielski, M., Filipowski, W., Michalski, R.: The Diffusion of Viral Content in Multi-layered Social Networks. ICCCI 2013, The 5th International Conference on Computational Collective Intelligence Technologies and Applications. Lecture Notes in Artificial Intelligence LNAI, vol. 8083, pp. 30-39, Springer, Berlin Heidelberg (2013) [abstract]
42. Jankowski, J., Ciuberek, S., Zbieg, A., Michalski, R.: Studying Paths of Participation in Viral Diffusion Process. SocInfo 2012 - The 4th International Conference on Social Informatics. Lecture Notes in Computer Science LNCS, vol. 7710, pp. 503-516, Springer, Berlin Heidelberg (2012) [abstract]
43. Jankowski, J., Michalski, R., Kazienko, P.: The Multidimensional Study of Viral Campaigns as Branching Processes. SocInfo 2012 - The 4th International Conference on Social Informatics. Lecture Notes in Computer Science LNCS, vol. 7710, pp. 462-474, Springer, Berlin Heidelberg (2012) [abstract]
44. Michalski, R., Bródka, P., Kazienko, P., Juszczyszyn, K.: Quantifying Social Network Dynamics. CASoN 2012, The Fourth International Conference on Computational Aspects of Social Networks. IEEE Computer Society, pp. 69-74 (2012) [abstract]
45. Michalski, R., Jankowski, J., Kazienko, P.: Negative Effects of Incentivised Viral Campaigns for Activity in Social Networks. SCA 2012, The 2nd International Conference on Social Computing and its Applications. IEEE Computer Society, pp. 391-398 (2012) [abstract]
46. Michalski, R., Kazienko, P.: Towards the Exception-Aware Workflow System. IADIS International Conference on Applied Computing 2012. Proceedings of the IADIS International Conference on Applied Computing, pp. 413-416 (2012) [abstract]
47. Michalski, R., Kazienko, P.: Gathering Information about Social Network Users by means of CSRF Attack. MISSI 2012, The 8th International Conference on Multimedia & Network Information Systems. Tempo, pp. 165-173 (2012) [abstract]
48. Zbieg, A., Żak, B., Jankowski, J., Michalski, R., Ciuberek, S.: Studying Diffusion of Viral Content at Dyadic Level. ASONAM 2012, The 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. IEEE Computer Society, pp. 1291-1297 (2012) [abstract]
49. Michalski, R., Kazienko, P., Król, D.: Predicting Social Network Measures using Machine Learning Approach. ASONAM 2012, The 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. IEEE Computer Society, pp. 1088-1091 (2012) [abstract]
50. Michalski, R., Bródka, P., Palus, S., Kazienko, P., Juszczyszyn, K.: Modelling Social Network Evolution. SocInfo 2011, The Third International Conference on Social Informatics. Lecture Notes in Computer Science LNCS, vol. 6984, pp. 283-286, Springer, Berlin Heidelberg (2011) [abstract]
51. Michalski, R., Palus, S., Kazienko, P.: Matching Organizational Structure and Social Network Extracted from Email Communication. BIS 2011, 14th International Conference on Business Information Systems. Lecture Notes in Business Information Processing LNBIP, vol. 87, pp. 197-206, Springer, Berlin Heidelberg (2011) [abstract]
52. Kazienko, P., Michalski, R., Palus, S.: Social Network Analysis as a Tool for Improving Enterprise Architecture. KES-AMSTA 2011, The 5th International KES Symposium on Agents and Multi-agent Systems - Technologies and Applications. Lecture Notes in Artificial Intelligence LNAI, vol. 6682, pp. 651-660, Springer, Berlin Heidelberg (2011) [abstract]

Michalski, R., Kazienko, P., Jankowski, J.

Convince a Dozen More and Succeed - The Influence in Multi-layered Social Networks

The Second Workshop on Complex Networks and their Applications at SITIS 2013 - The 9th International Conference on Signal Image Technology & Internet based Systems, December 2-5 2013, Kyoto, Japan, IEEE Computer Society, pp. 499-505 (2013)

Abstract. Humans utilise multiple communication channels in their social interactions and also information diffusion as well as the spread of influence are practically related with many contexts. Each such context (channel) may represent a different communication method or a different environment of a given person. This facilitates building multiple social networks, that are not independent. They share the same set of nodes connected with many links grounded on different layers - these networks are called multi-layered or multiplex social networks. The influence process may vary in these kinds of social networks depending on the network model, the level of influence for each layer and other factors such as the overlap of nodes and links across layers. In this paper, the influence processes in multi-layered social networks have been analysed showing that for almost all analysed network models, the success in convincing few more individuals may be crucial for the whole influence process. The results revealed that the process is not linear in terms of relation between the number of initially influenced individuals and the total number of influenced nodes. The linear threshold model has been utilized as a base influence model.

Keywords: social network analysis, multi-layered networks, multiplex networks, spread of influence, linear threshold

Jankowski, J., Michalski, R., Kazienko, P.

Compensatory Seeding in Networks with Varying Availability of Nodes

Proceedings of the Workshop on the Semantic and Dynamic Analysis of Information Networks collocated with the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, August 25-28 2013, Niagara Falls, Canada, IEEE Computer Society, pp. 1242-1249 (2013)

Abstract. Diffusion of information in social networks takes more and more attention from marketers. New methods and algorithms are constantly developed towards maximizing reach of the campaigns and increasing their effectiveness. One of the important research directions in this area is related to selecting initial nodes of the campaign to result with maximizing its effects represented as total number of infections. To achieve this goal, several strategies were developed and they are based on different network measures and other characteristics of users. The problem is that most of these strategies base on static network properties while typical online networks change over time and are sensitive to varying activity of users. In this work a novel strategy is proposed which is based on multiple measures with additional parameters related to nodes availability in time periods prior to the campaign. Presented results show that it is possible to com-pensate users with high network measures by others having high frequency of system usage, which, instead, may be easier or cheaper to acquire.

Keywords: social network analysis, viral marketing, seeding strategies, information diffusion

Jankowski, J., Kozielski, M., Filipowski, W., Michalski, R.

The Diffusion of Viral Content in Multi-layered Social Networks

ICCCI 2013, The 5th International Conference on Computational Collective Intelligence Technologies and Applications. Lecture Notes in Artificial Intelligence LNAI, vol. 8083, pp. 30-39, Springer, Berlin Heidelberg (2013)

Abstract. Modelling the diffusion of information is one of the key areas related to activity within social networks. In this field, there is recent research associated with the use of community detection algorithms and the analysis of how the structure of communities is affecting the spread of information. The purpose of this article is to examine the mecha-nisms of diffusion of viral content with particular emphasis on cross community diffusion.

Keywords: diffusion of information, multi-layered social networks, clustering algorithms, multiplex networks, social network analysis

Jankowski, J., Ciuberek, S., Zbieg, A., Michalski, R.

Studying Paths of Participation in Viral Diffusion Process

SocInfo 2012 - The 4th International Conference on Social Informatics. Lecture Notes in Computer Science LNCS, vol. 7710, pp. 503-516, Springer, Berlin Heidelberg (2012)

Abstract. Authors propose a conceptual model of participation in viral diffusion process composed of four stages: awareness, infection, engagement and action. To verify the model it has been applied and studied in the virtual social chat environment settings. The study investigates the behavioural paths of actions that reflect the stages of participation in the diffusion and presents shortcuts, that lead to the final action – the attendance in a virtual event. The results show that the participation in each stage of the process increases the probability of reaching the final action. Nevertheless, the majority of users involved in the virtual event did not go through each stage of the process but followed the shortcuts. That suggests that the viral diffusion process is not necessarily a linear sequence of human actions but rather a dynamic system.

Keywords: information diffusion, online social networks, participation model, multistage analysis

Jankowski, J., Michalski, R., Kazienko, P.

The Multidimensional Study of Viral Campaigns as Branching Processes

SocInfo 2012 - The 4th International Conference on Social Informatics. Lecture Notes in Computer Science LNCS, vol. 7710, pp. 462-474, Springer, Berlin Heidelberg (2012)

Abstract. Viral campaigns on the Internet may follow variety of models, depending on the content, incentives, personal attitudes of sender and recipient to the content and other factors. Due to the fact that the knowledge of the campaign specifics is essential for the campaign managers, researchers are constantly evaluating models and real-world data. The goal of this article is to present the new knowledge obtained from studying two viral campaigns that took place in a virtual world which followed the branching process. The results show that it is possible to reduce the time needed to estimate the model parameters of the campaign and, moreover, some important aspects of time-generations relationship are presented.

Keywords: viral campaigns, diffusion of information, branching process, social network analysis, virtual worlds

Michalski, R., Bródka, P., Kazienko, P., Juszczyszyn, K.

Quantifying Social Network Dynamics

CASoN 2012, The Fourth International Conference on Computational Aspects of Social Networks. IEEE Computer Society, pp. 69-74 (2012)

Abstract. The dynamic character of most social networks requires to model evolution of networks in order to enable complex analysis of theirs dynamics. The following paper focuses on the definition of differences between network snapshots by means of Graph Differential Tuple. These differences enable to calculate the diverse distance measures as well as to investigate the speed of changes. Four separate measures are suggested in the paper with experimental study on real social network data.

Keywords: social network changes, graph differential tuple, dynamics of the social network, SNA, graph edit distance

Michalski, R., Jankowski, J., Kazienko, P.

Negative Effects of Incentivised Viral Campaigns for Activity in Social Networks

SCA 2012, The 2nd International Conference on Social Computing and its Applications. IEEE Computer Society, pp. 391-398 (2012)

Abstract. Viral campaigns are crucial methods for word-of-mouth marketing in social communities. The goal of these campaigns is to encourage people for activity. The problem of incentivised and non-incentivised campaigns is studied in the paper. Based on the data collected within the real social networking site both approaches were compared. The experimental results revealed that a highly motivated campaign not necessarily provides better results due to overlapping effect. Additional studies have shown that the behaviour of individual community members in the campaign based on their service profile can be predicted but the classification accuracy may be limited.

Keywords: social network analysis, incentivised campaigns effectiveness, viral campaigns, viral marketing

Michalski, R., Kazienko, P.

Towards the Exception-Aware Workflow System

IADIS International Conference on Applied Computing 2012. Proceedings of the IADIS International Conference on Applied Computing, pp. 413-416 (2012)

Abstract. Nowadays, the workflow systems are being introduced to more and more companies. Due to the fact that it is hard to define a process which avoids any exceptions, sometimes exceptions simply occur. The goal of this paper is to introduce a novel method of predicting the exceptions by using extended set of information gathered in the data mining process and to signal the possibility of the exception occurrence at the earliest moment in time. To further extend the proposed approach, social network analysis is used to suggest the flow of the exception in the workflow system. Together, these new workflow extensions may lead to shortening the time of exception handling. This paper describes also a set of changes introduced in a real-world workflow system to benefit from the proposed approach, making it also more suitable for practitioners.

Keywords: workflow systems, exceptions, data mining, SNA

Kazienko, P., Michalski, R., Palus, S.

Matching Organizational Structure and Social Network Extracted from Email Communication

BIS 2011, 14th International Conference on Business Information Systems. Lecture Notes in Business Information Processing LNBIP, vol. 87, pp. 197-206, Springer, Berlin Heidelberg (2011)

Abstract. The following paper presents the concept of matching social network and corporate hierarchy in organizations with stable corporate structure. The idea allows to confirm whether social position of an employee calculated on the basis of the social network differs significantly from the formal employee role in the company. The results of such analysis may lead to possible company management improvement enabling to gain a competitive edge. In order to perform this task the authors have made experiments with the use of two real-life datasets: Enron and mid-sized manufacturing companies showing which social network metrics may be suitable to match organizational structure and social network with good results.

Keywords: social network analysis, organizational design, enterprise management, corporate social networks, employee position evaluation

Kazienko, P., Michalski, R., Palus, S.

Social Network Analysis as a Tool for Improving Enterprise Architecture

KES-AMSTA 2011, The 5th International KES Symposium on Agents and Multi-agent Systems - Technologies and Applications. Lecture Notes in Artificial Intelligence LNAI, vol. 6682, pp. 651-660, Springer, Berlin Heidelberg (2011)

Abstract. The paper provides the overview of essential analyses and methods, helpful for enterprise architecture improvement and based on social network approach. The ideas presented in this paper focus on social network, that is built with the use of real-life manufacturing company data. It has been shown that corporate social network analysis, as a decision support system, may be influential for managing a company. Several ideas, measurements, interpretations and evaluation methods are given and discussed, in particular centrality degree, social network extraction, process management.

Michalski, R., Kazienko, P.

Gathering Information about Social Network Users by means of CSRF Attack

MISSI 2012, The 8th International Conference on Multimedia & Network Information Systems. Tempo, pp. 165-173 (2012)

Abstract. Nowadays, while the privacy risks of social networking sites are being considered as important also by regular users, some of those users are trying to protect their real identity by using aliases, abbreviated names etc. In that case they are still able to use social networking sites, while having a very misleading awareness of privacy. Some more sophisticated users are also using different aliases in every social network service they are members of. That is why the task to discover the same identity with its neighbourhood among variety of sites is being more and more complicated. The following paper presents a concept based on cross-site request forgery attack to make the automation of that task very effective.

Keywords: CSRF attack, social networking websites, social networks, cross-site request forgery

Michalski, R., Bródka, P., Palus, S., Kazienko, P., Juszczyszyn, K.

Modelling Social Network Evolution

SocInfo 2011, The Third International Conference on Social Informatics. Lecture Notes in Computer Science LNCS, vol. 6984, pp. 283-286, Springer, Berlin Heidelberg (2011)

Abstract. Most of the real social networks extracted from various data sources evolve and change their profile over time. For that reason, there is a great need to model evolution of networks in order to enable complex analyses of theirs dynamics. The model presented in the paper focuses on definition of differences between following network snapshots by means of Graph Differential Tuple.

Keywords: social network evolution, graph distance measures

Michalski, R., Kazienko, P., Król, D.

Predicting Social Network Measures using Machine Learning Approach

ASONAM 2012, The 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. IEEE Computer Society, pp. 1088-1091 (2012)

Abstract. The link prediction problem in social networks defined as a task to predict whether a link between two particular nodes will appear in the future is still a broadly researched topic in the field of social network analysis. However, another relevant problem is solved in the paper instead of individual link forecasting: prediction of key network measures values, what is a more time saving approach. Two machine learning techniques were examined: time series forecasting and classification. Both of them were tested on two real-life social network datasets.

Keywords: social network, social network analysis, social networks measures, time series forecasting, classification

Zbieg, A., Żak, B., Jankowski, J., Michalski, R., Ciuberek, S.

Studying Diffusion of Viral Content at Dyadic Level

ASONAM 2012, The 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. IEEE Computer Society, pp. 1088-1091 (2012)

Abstract. Diffusion of information and viral content, social contagion and influence are still topics of broad evaluation. As theory explaining the role of influentials moves slightly to reduce their importance in the propagation of viral content, authors of the following paper have studied the information epidemic in a social networking platform in order to confirm recent theoretical findings in this area. While most of related experiments focus on the level of individuals, the elementary entities of the following analysis are dyads. The authors study behavioral motifs that are possible to observe at the dyadic level. The study shows significant differences between dyads that are more vs less engaged in the diffusion process. Dyads that fuel the diffusion proccess are characterized by stronger relationships (higher activity, more common friends), more active and networked receiving party (higher centrality measures), and higher authority centrality of person sending a viral message.

Keywords: diffusion of information, viral content, dyads, motif analysis, influentials, influence factors, social networks, virtual worlds

Michalski, R., Kajdanowicz, T., Bródka, P., Kazienko, P.

Seed Selection for Spread of Influence in Social Networks: Temporal vs. Static Approach

New Generation Computing (JCR-listed journal), Vol. 32, Issue 3-4, pp. 213-235. Ohmsha-Japan and Springer (2014)

Abstract. The problem of finding optimal set of users for influencing others in the social network has been widely studied. Because it is NP-hard, some heuristics were proposed to find sub-optimal solutions. Still, one of the commonly used assumption is the one that seeds are chosen on the static network, not the dynamic one. This static approach is in fact far from the real-world networks, where new nodes may appear and old ones dynamically disappear in course of time.

The main purpose of this paper is to analyse how the results of one of the typical models for spread of influence - linear threshold - differ depending on the strategy of building the social network used later for choosing seeds. To show the impact of network creation strategy on the final number of influenced nodes - outcome of spread of influence, the results for three approaches were studied: one static and two temporal with different granularities, i.e. various number of time windows. Social networks for each time window encapsulated dynamic changes in the network structure. Calculation of various node structural measures like degree or betweenness respected these changes by means of forgetting mechanism - more recent data had greater influence on node measure values. These measures were, in turn, used for node ranking and their selection for seeding.

All concepts were applied to experimental verification on five real datasets. The results revealed that temporal approach is always better than static and the higher granularity in the temporal social network while seeding, the more finally influenced nodes. Additionally, outdegree measure with exponential forgetting typically outperformed other time-dependent structural measures, if used for seed candidate ranking.

Keywords: social networks, complex networks, spread of influence, seeding strategies, seed ranking, node selection, temporal networks, temporal complex networks, temporal granularity, network measures

Kajdanowicz, T., Michalski, R., Musiał, K., Kazienko, P.

Learning in Unlabelled Networks - An Active Learning and Inference Approach

AI Communications (JCR-listed journal), Vol. 29, No. 1, pp. 123-148. IOS Press (2015)

Abstract. The task of determining labels of all network nodes based on the knowledge about network structure and labels of some training subset of nodes is called the within-network classification. It may happen that none of the labels of the nodes is known and additionally there is no information about number of classes to which nodes can be assigned. In such a case a subset of nodes has to be selected for initial label acquisition. The question that arises is: "labels of which nodes should be collected and used for learning in order to provide the best classification accuracy for the whole network?". Active learning and inference is a practical framework to study this problem. A set of methods for active learning and inference for within network classification is proposed and validated. The utility score calculation for each node based on network structure is the first step in the process. The scores enable to rank the nodes. Based on the ranking, a set of nodes, for which the labels are acquired, is selected (e.g. by taking top or bottom N from the ranking). The new measure-neighbour methods proposed in the paper suggest not obtaining labels of nodes from the ranking but rather acquiring labels of their neighbours. The paper examines 29 distinct formulations of utility score and selection methods reporting their impact on the results of two collective classification algorithms: Iterative Classification Algorithm and Loopy Belief Propagation. We advocate that the accuracy of presented methods depends on the structural properties of the examined network. We claim that measure-neighbour methods will work better than the regular methods for networks with higher clustering coefficient and worse than regular methods for networks with low clustering coefficient. According to our hypothesis, based on clustering coefficient we are able to recommend appropriate active learning and inference method..

Keywords: collective classification, relational learning, complex networks, loopy belief propagation