Relevance Search on Signed Heterogeneous Information Network Based on Meta-path Factorization

Relevance search is a primitive operation in heterogeneous information networks, where the task is to measure the relatedness of objects with different types. Due to the semantics implied by network links, conventional research on relevance search is often based on meta-path in heterogeneous information networks. However, existing approaches mainly focus on studying non-signed information networks, without considering the polarity of the links in the network. In reality, there are many signed heterogeneous networks that the links can be either positive (such as trust, preference, friendship, etc.) or negative (such as distrust, dislike, opposition, etc.). It is challenging to utilize the semantic information of the two kinds of links in meta-paths and integrate them in a unified way to measure relevance.

[1]  Philip S. Yu,et al.  Integrating meta-path selection with user-guided object clustering in heterogeneous information networks , 2012, KDD.

[2]  Ni Lao,et al.  Fast query execution for retrieval models based on path-constrained random walks , 2010, KDD.

[3]  Minghua Chen,et al.  Predicting positive and negative links in signed social networks by transfer learning , 2013, WWW.

[4]  Jure Leskovec,et al.  Predicting positive and negative links in online social networks , 2010, WWW '10.

[5]  Xiaowei Xu,et al.  SCAN: a structural clustering algorithm for networks , 2007, KDD '07.

[6]  Zhoujun Li,et al.  Burst Time Prediction in Cascades , 2015, AAAI.

[7]  Philip S. Yu,et al.  Mining Knowledge from Interconnected Data: A Heterogeneous Information Network Analysis Approach , 2012, Proc. VLDB Endow..

[8]  Charu C. Aggarwal,et al.  When will it happen?: relationship prediction in heterogeneous information networks , 2012, WSDM '12.

[9]  Charu C. Aggarwal,et al.  Co-author Relationship Prediction in Heterogeneous Bibliographic Networks , 2011, 2011 International Conference on Advances in Social Networks Analysis and Mining.

[10]  Xiang Li,et al.  On link-based similarity join , 2011, Proc. VLDB Endow..

[11]  Panagiotis Symeonidis,et al.  Transitive node similarity for link prediction in social networks with positive and negative links , 2010, RecSys '10.

[12]  Nitesh V. Chawla,et al.  New perspectives and methods in link prediction , 2010, KDD.

[13]  Aravind Srinivasan,et al.  Predicting Trust and Distrust in Social Networks , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[14]  Jennifer Widom,et al.  SimRank: a measure of structural-context similarity , 2002, KDD.

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

[16]  Christos Faloutsos,et al.  Fast Random Walk with Restart and Its Applications , 2006, Sixth International Conference on Data Mining (ICDM'06).

[17]  Philip S. Yu,et al.  HeteSim: A General Framework for Relevance Measure in Heterogeneous Networks , 2013, IEEE Transactions on Knowledge and Data Engineering.

[18]  David Liben-Nowell,et al.  The link-prediction problem for social networks , 2007 .