Information Diffusion at Workplace

People nowadays need to spend a large amount of time on their work everyday and workplace has become an important social occasion for effective communication and information exchange among employees. Besides traditional online contacts (e.g., face-to-face meetings and telephone calls), to facilitate the communication and cooperation among employees, a new type of online social networks has been launched inside the firewalls of many companies, which are named as the "enterprise social networks" (ESNs). In this paper, we want to study the information diffusion among employees at workplace via both online ESNs and online contacts. This is formally defined as the IDE (Information Diffusion in Enterprise) problem. Several challenges need to be addressed in solving the IDE problem: (1) diffusion channel extraction from online ESN and online contacts; (2) effective aggregation of the information delivered via different diffusion channels; and (3) communication channel weighting and selection. A novel information diffusion model, Muse (Multi-source Multi-channel Multi-topic diffUsion SElection), is introduced in this paper to resolve these challenges. Extensive experiments conducted on real-world ESN and organizational chart dataset demonstrate the outstanding performance of Muse in addressing the IDE problem.

[1]  Yizhou Sun,et al.  Modeling Topic Diffusion in Multi-Relational Bibliographic Information Networks , 2014, CIKM.

[2]  Matthew Richardson,et al.  Mining the network value of customers , 2001, KDD '01.

[3]  Pernilla Qvarfordt,et al.  Exploring the workplace communication ecology , 2010, CHI.

[4]  Philip S. Yu,et al.  Discover Tipping Users For Cross Network Influencing , 2016 .

[5]  Philip S. Yu,et al.  Enterprise Social Link Recommendation , 2015, CIKM.

[6]  Philip S. Yu,et al.  Extracting social events for learning better information diffusion models , 2013, KDD.

[7]  E B Keeler,et al.  The value of remaining lifetime is close to estimated values of life. , 2001, Journal of health economics.

[8]  My T. Thai,et al.  Least Cost Influence in Multiplex Social Networks: Model Representation and Analysis , 2013, 2013 IEEE 13th International Conference on Data Mining.

[9]  Philip S. Yu,et al.  Discover Tipping Users for Cross Network Influencing (Invited Paper) , 2016, 2016 IEEE 17th International Conference on Information Reuse and Integration (IRI).

[10]  Éva Tardos,et al.  Maximizing the Spread of Influence through a Social Network , 2015, Theory Comput..

[11]  Jon M. Kleinberg,et al.  The small-world phenomenon: an algorithmic perspective , 2000, STOC '00.

[12]  Matthew Richardson,et al.  Mining knowledge-sharing sites for viral marketing , 2002, KDD.

[13]  Philip S. Yu,et al.  Meta-path based multi-network collective link prediction , 2014, KDD.

[14]  Philip S. Yu,et al.  A Survey of Heterogeneous Information Network Analysis , 2015, IEEE Transactions on Knowledge and Data Engineering.

[15]  Viktor K. Prasanna,et al.  The role of organization hierarchy in technology adoption at the workplace , 2013, 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013).

[16]  Dimitri P. Bertsekas,et al.  Constrained Optimization and Lagrange Multiplier Methods , 1982 .

[17]  Lada A. Adamic,et al.  The role of social networks in information diffusion , 2012, WWW.

[18]  Philip S. Yu,et al.  PathSim , 2011, Proc. VLDB Endow..

[19]  Philip S. Yu,et al.  Organizational Chart Inference , 2015, KDD.

[20]  Jimeng Sun,et al.  Social influence analysis in large-scale networks , 2009, KDD.

[21]  Jure Leskovec,et al.  Information diffusion and external influence in networks , 2012, KDD.

[22]  Philip S. Yu,et al.  Inferring anchor links across multiple heterogeneous social networks , 2013, CIKM.

[23]  Robert A. Stine,et al.  A Note on Deriving the Information Matrix for a Logistic Distribution , 1986 .

[24]  Philip S. Yu,et al.  Influence Maximization Across Partially Aligned Heterogenous Social Networks , 2015, PAKDD.