Publications

User Community Detection via Embedding of Social Network Structure and Temporal Content

Hossein Fani and Eric Jiang and Ebrahim Bagheri and Feras Al-Obeidat and Weichang Du and Mehdi Kargar
Reference:
Hossein Fani; Eric Jiang; Ebrahim Bagheri; Feras Al-Obeidat; Weichang Du and Mehdi Kargar User Community Detection via Embedding of Social Network Structure and Temporal Content. In Information Processing and Management, 2019.
Links to Publication: [www][pdf]
Abstract:
Identifying and extracting user communities is an important step towards understanding social network dynamics from a macro perspective. For this reason, the work in this paper explores various aspects related to the identification of user communities. To date, user community detection methods employ either explicit links between users (link analysis), or users' topics of interest in posted content (content analysis), or in tandem. Little work has considered temporal evolution when identifying user communities in a way to group together those users who share not only similar topical interests but also similar temporal behavior towards their topics of interest. In this paper, we identify user communities through textitmultimodal feature learning (embeddings). Our core contributions can be enumerated as (a) we propose a new method for learning neural embeddings for users based on their temporal content similarity; (b) we learn user embeddings based on their social network connections (links) through neural graph embeddings; (c) we systematically interpolate temporal content-based embeddings and social link-based embeddings to capture both social network connections and temporal content evolution for representing users, and (d) we systematically evaluate the quality of each embedding type in isolation and also when interpolated together and demonstrate their performance on a Twitter dataset under two different application scenarios, namely textitnews recommendation and textituser prediction. We find that (1) content-based methods produce higher quality communities compared to link-based methods; (2) methods that consider temporal evolution of content, our proposed method in particular, show better performance compared to their non-temporal counter-parts; (3) communities that are produced when time is explicitly incorporated in user vector representations have higher quality than the ones produced when time is incorporated into a generative process, and finally (4) while link-based methods are weaker than content-based methods, their interpolation with content-based methods leads to improved quality of the identified communities.
Bibtex Entry:
@article{ipm2019d, title={User Community Detection via Embedding of Social Network Structure and Temporal Content}, journal={Information Processing and Management}, author={Hossein Fani and Eric Jiang and Ebrahim Bagheri and Feras Al-Obeidat and Weichang Du and Mehdi Kargar}, abstract = {Identifying and extracting user communities is an important step towards understanding social network dynamics from a macro perspective. For this reason, the work in this paper explores various aspects related to the identification of user communities. To date, user community detection methods employ either explicit links between users (link analysis), or users' topics of interest in posted content (content analysis), or in tandem. Little work has considered temporal evolution when identifying user communities in a way to group together those users who share not only similar topical interests but also similar temporal behavior towards their topics of interest. In this paper, we identify user communities through \textit{multimodal} feature learning (embeddings). Our core contributions can be enumerated as (a) we propose a new method for learning neural embeddings for users based on their temporal content similarity; (b) we learn user embeddings based on their social network connections (links) through neural graph embeddings; (c) we systematically interpolate temporal content-based embeddings and social link-based embeddings to capture both social network connections and temporal content evolution for representing users, and (d) we systematically evaluate the quality of each embedding type in isolation and also when interpolated together and demonstrate their performance on a Twitter dataset under two different application scenarios, namely \textit{news recommendation} and \textit{user prediction}. We find that (1) content-based methods produce higher quality communities compared to link-based methods; (2) methods that consider temporal evolution of content, our proposed method in particular, show better performance compared to their non-temporal counter-parts; (3) communities that are produced when time is explicitly incorporated in user vector representations have higher quality than the ones produced when time is incorporated into a generative process, and finally (4) while link-based methods are weaker than content-based methods, their interpolation with content-based methods leads to improved quality of the identified communities.} year = {2019}, webpdf={http://ls3.rnet.ryerson.ca/wiki/images/8/87/Ipm_fani2019.pdf}, url={https://www.journals.elsevier.com/information-processing-and-management} }




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