Graph Community Discovery Algorithms in Neo4j with a Regularization-based Evaluation Metric

Community discovery is central to social network analysis as it provides a natural way for decomposing a social graph to smaller ones based on the interactions among individuals. Communities do not need to be disjoint and often exhibit recursive structure. The latter has been established as a distinctive characteristic of large social graphs, indicating a modularity in the way humans build societies. This paper presents the implementation of four established community discovery algorithms in the form of Neo4j higher order analytics with the Twitter4j Java API and their application to two real Twitter graphs with diverse structural properties. In order to evaluate the results obtained from each algorithm a regularization-like metric, balancing the global and local graph self-similarity akin to the way it is done in signal processing, is proposed.

[1]  Vasileios Megalooikonomou,et al.  Higher order graph centrality measures for Neo4j , 2015, 2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA).

[2]  Qi He,et al.  TwitterRank: finding topic-sensitive influential twitterers , 2010, WSDM '10.

[3]  C. Vogel.  Computational Methods for Inverse Problems , 1987 .

[4]  Christos Makris,et al.  T-PICE: Twitter Personality Based Influential Communities Extraction System , 2014, 2014 IEEE International Congress on Big Data.

[5]  Vasileios Megalooikonomou,et al.  Regularizing large biosignals with finite differences , 2016, 2016 7th International Conference on Information, Intelligence, Systems & Applications (IISA).

[6]  Scott Counts,et al.  Identifying topical authorities in microblogs , 2011, WSDM '11.

[7]  Gilad Mishne,et al.  Finding high-quality content in social media , 2008, WSDM '08.

[8]  S.,et al.  An Efficient Heuristic Procedure for Partitioning Graphs , 2022 .

[9]  Tor Arne Johansen,et al.  On Tikhonov regularization, bias and variance in nonlinear system identification , 1997, Autom..

[10]  Christos Makris,et al.  Comparing Algorithmic Principles for Fuzzy Graph Communities over Neo4j , 2017 .

[11]  John Scott.  Social Network Analysis , 1988 .

[12]  Matthieu Latapy,et al.  Computing Communities in Large Networks Using Random Walks , 2004, J. Graph Algorithms Appl..

[13]  Luciano Rossoni,et al.  Models and methods in social network analysis , 2006 .

[14]  Mark Newman,et al.  Detecting community structure in networks , 2004 .

[15]  Eugene Agichtein,et al.  Discovering authorities in question answer communities by using link analysis , 2007, CIKM '07.

[16]  M. Benzi,et al.  Quadrature rule-based bounds for functions of adjacency matrices , 2010 .

[17]  Ioannis Hatzilygeroudis,et al.  Conversation Emotional Modeling in Social Networks , 2014, 2014 IEEE 26th International Conference on Tools with Artificial Intelligence.

[18]  Jean-Loup Guillaume,et al.  Fast unfolding of community hierarchies in large networks , 2008, ArXiv.

[19]  Jim Webber,et al.  Graph Databases: New Opportunities for Connected Data , 2013 .

[20]  Tomaso A. Poggio,et al.  Regularization Theory and Neural Networks Architectures , 1995, Neural Computation.

[21]  Mark Newman,et al.  Networks: An Introduction , 2010 .

[22]  M E J Newman,et al.  Fast algorithm for detecting community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[23]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[24]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[25]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[26]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[27]  Onofrio Panzarino,et al.  Learning Cypher , 2014 .

[28]  H. Attouch,et al.  Approximation and regularization of arbitrary functions in Hilbert spaces by the Lasry-Lions method , 1993 .

[29]  Christos Makris,et al.  On converting community detection algorithms for fuzzy graphs in Neo4j , 2016, ArXiv.

[30]  Amy Nicole Langville,et al.  Google's PageRank and beyond - the science of search engine rankings , 2006 .