Understanding the Psycho-Sociological Facets of Homophily in Social Network Communities

"Community" in social networks is a nebulous concept. A community is generally assumed to be formed by people who possess similar attributes or characteristics, also known as "homophily". Although there has been a lot of research on community detection based on network topology, the semantic interpretation of communities is rarely studied. The present work aims to understand the behavioral similarity of users present in their personal neighborhood communities formed by friends, relatives, or colleagues, and addresses two fundamental questions: (i) Are communities formed by users who possess similar behavioral traits? If so, does this apply to all those sub-networks, i.e., friends, relatives, and colleagues? (ii) Does adding behavioral node-specific attributes/features to the nodes in a network lead to better community detection? To better understand the psycho-sociological homophilic nature of personal networks, the personalities and values of Twitter users were analyzed using the well-established "Big-5 personality model" and "Schwartz sociological behavior model". Empirical results based on the psychosociological behavior show that friends networks exhibit homophily, whereas relatives and colleagues networks do not exhibit such homophilic behavior. It can also be observed that neurotic people tend to behave heterogeneously with people of various personality traits. In addition, it is shown that such empirical evidence can be used as features for the tasks of community detection and link prediction.

[1]  Shyhtsun Felix Wu,et al.  Analysis of user keyword similarity in online social networks , 2011, Social Network Analysis and Mining.

[2]  M. Newman.  Clustering and preferential attachment in growing networks. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  Walter Daelemans,et al.  Ensemble Methods for Personality Recognition , 2013, ICWSM 2013.

[4]  Rada Mihalcea,et al.  Disentangling Topic Models: A Cross-cultural Analysis of Personal Values through Words , 2016, NLP+CSS@EMNLP.

[5]  Ling Chen,et al.  Link Prediction in Networks with Nodes Attributes by Similarity Propagation , 2015, ArXiv.

[6]  Erik Cambria,et al.  AFFECTIVE COMPUTI G AND SENTIMENT ANALYSIS Deep Learning-Based Document Modeling for Personality Detection from Text , 2017 .

[7]  Benno Stein,et al.  Overview of the 4th Author Profiling Task at PAN 2016: Cross-Genre Evaluations , 2016, CLEF.

[8]  Jeromy Anglim,et al.  HEXACO personality and Schwartz's personal values: A facet-level analysis , 2017 .

[9]  Zhanyi Hu,et al.  Physiognomy: Personality traits prediction by learning , 2017, Int. J. Autom. Comput..

[10]  Tanmoy Chakraborty,et al.  Socio-Ethnic Ingredients of Social Network Communities , 2017, CSCW Companion.

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

[12]  Mihai Gavrilescu,et al.  Predicting the Big Five personality traits from handwriting , 2018, EURASIP J. Image Video Process..

[13]  Steven Skiena,et al.  DeepWalk: online learning of social representations , 2014, KDD.

[14]  Prerna Agarwal,et al.  DyPerm: Maximizing Permanence for Dynamic Community Detection , 2018, PAKDD.

[15]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[16]  Jure Leskovec,et al.  Community Detection in Networks with Node Attributes , 2013, 2013 IEEE 13th International Conference on Data Mining.

[17]  Marti A. Hearst.  Trends & Controversies: Support Vector Machines , 1998, IEEE Intell. Syst..

[18]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

[19]  Adrian E. Raftery,et al.  Representing degree distributions, clustering, and homophily in social networks with latent cluster random effects models , 2009, Soc. Networks.

[20]  Junjie Wu,et al.  Embedding Temporal Network via Neighborhood Formation , 2018, KDD.

[21]  Ravi Kumar,et al.  Influence and correlation in social networks , 2008, KDD.

[22]  Tanmoy Chakraborty,et al.  Revealing Psycholinguistic Dimensions of Communities in Social Networks , 2018, IEEE Intelligent Systems.

[23]  Peter H. Reingen,et al.  Social Ties and Word-of-Mouth Referral Behavior , 1987 .

[24]  Ray Reagans.  Demographic Diversity as Network Connections: Homophily and the Diversity–Performance Debate , 2013 .

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

[26]  Leo Katz,et al.  A new status index derived from sociometric analysis , 1953 .

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

[28]  Stanford,et al.  Learning to Discover Social Circles in Ego Networks , 2012 .

[29]  Linyuan Lü,et al.  Similarity index based on local paths for link prediction of complex networks. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[30]  Sinan Aral,et al.  Identifying Influential and Susceptible Members of Social Networks , 2012, Science.

[31]  Sanjukta Bhowmick,et al.  GenPerm: A Unified Method for Detecting Non-Overlapping and Overlapping Communities , 2016, IEEE Transactions on Knowledge and Data Engineering.

[32]  Hosung Park,et al.  What is Twitter, a social network or a news media? , 2010, WWW '10.

[33]  Amitava Das,et al.  Inducing Personalities and Values from Language Use in Social Network Communities , 2018, Inf. Syst. Frontiers.

[34]  Kevin Chen-Chuan Chang,et al.  From Node Embedding To Community Embedding , 2016, ArXiv.

[35]  Michel Verleysen,et al.  Improving individual predictions using social networks assortativity , 2017, 2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM).

[36]  L. C. Martin,et al.  Using information theory to search for co-evolving residues in proteins , 2005, Bioinform..

[37]  Gözde Özbal,et al.  Sensicon: An Automatically Constructed Sensorial Lexicon , 2014, EMNLP.

[38]  Liang Gao,et al.  A General Personality Prediction Framework Based on Facebook Profiles , 2018, ICMLC.

[39]  Carlo Strapparava,et al.  WordNet Affect: an Affective Extension of WordNet , 2004, LREC.

[40]  Nitin Agarwal,et al.  A study of homophily on social media , 2012, World Wide Web.

[41]  Tanmoy Chakraborty,et al.  Semantic Interpretation of Social Network Communities , 2017, AAAI.

[42]  Pietro Liò,et al.  Towards real-time community detection in large networks. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[43]  M. Newman,et al.  Finding community structure in very large networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[44]  Daniel Danner,et al.  Relationships between Personality and Cognitive Ability: A Facet-Level Analysis , 2018, Journal of Intelligence.

[45]  M E J Newman.  Assortative mixing in networks. , 2002, Physical review letters.

[46]  Hsinchun Chen,et al.  Recommendation as link prediction in bipartite graphs: A graph kernel-based machine learning approach , 2013, Decis. Support Syst..

[47]  Huan Liu,et al.  Community Detection and Mining in Social Media , 2010, Community Detection and Mining in Social Media.

[48]  Jian Pei,et al.  Community Preserving Network Embedding , 2017, AAAI.

[49]  Faraz Zaidi,et al.  Generating online social networks based on socio-demographic attributes , 2014, J. Complex Networks.

[50]  Fabio Pianesi,et al.  The Workshop on Computational Personality Recognition 2014 , 2014, ACM Multimedia.

[51]  Dickson K. W. Chiu,et al.  Identifying Personality-Based Communities in Social Networks , 2013, ER Workshops.

[52]  Inderjit S. Dhillon,et al.  Weighted Graph Cuts without Eigenvectors A Multilevel Approach , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[53]  Jure Leskovec,et al.  node2vec: Scalable Feature Learning for Networks , 2016, KDD.

[54]  L. Hubert,et al.  Comparing partitions , 1985 .

[55]  Aishwarya N. Reganti,et al.  A Societal Sentiment Analysis: Predicting the Values and Ethics of Individuals by Analysing Social Media Content , 2017, EACL.

[56]  Lada A. Adamic,et al.  Friends and neighbors on the Web , 2003, Soc. Networks.

[57]  S. Schwartz.  Universals in the Content and Structure of Values: Theoretical Advances and Empirical Tests in 20 Countries , 1992 .

[58]  Niloy Ganguly,et al.  Metrics for Community Analysis , 2016, ACM Comput. Surv..

[59]  Andrea Esuli,et al.  SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining , 2010, LREC.

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

[61]  Fabio Pianesi,et al.  Workshop on Computational Personality Recognition: Shared Task , 2013, Proceedings of the International AAAI Conference on Web and Social Media.

[62]  Kevin Chen-Chuan Chang,et al.  Learning Community Embedding with Community Detection and Node Embedding on Graphs , 2017, CIKM.

[63]  Claudio Castellano,et al.  Defining and identifying communities in networks. , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[64]  Jure Leskovec,et al.  Effects of user similarity in social media , 2012, WSDM '12.

[65]  Jure Leskovec,et al.  Defining and Evaluating Network Communities Based on Ground-Truth , 2012, ICDM.

[66]  A. Tellegen,et al.  PERSONALITY PROCESSES AND INDIVIDUAL DIFFERENCES An Alternative "Description of Personality": The Big-Five Factor Structure , 2022 .

[67]  Jia Xu,et al.  Personality Trait Prediction Based on 2.5D Face Feature Model , 2018, ICCCS.

[68]  Sanjukta Bhowmick,et al.  On the permanence of vertices in network communities , 2014, KDD.