Investigating community structure in perspective of ego network

Biswas, Anupam; Biswas, Bhaskar

doi:10.1016/j.eswa.2015.05.009

Cited by 45 publications

(15 citation statements)

References 59 publications

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“…Moreover, some works can be found that explore the problem of detecting communities based on how users have rated different objects [72]; Biswas and Biswas [14] perform community detection from an ego network perspective based on relationships mutual interest and compare it with six different state-of-the-art algorithms; and Moradi and Rostami [64] apply ant colony optimisation techniques for graph clustering and community detection.…”

Section: Community Detectionmentioning

confidence: 99%

Beyond social graphs: mining patterns underlying social interactions

Baldominos

Calle

Cuadra

2016

Pattern Anal Applic

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This work aims at discovering and extracting relevant patterns underlying social interactions. To do so, some knowledge extracted from Facebook, a social networking site, is formalised by means of an Extended Social Graph, a data structure which goes beyond the original concept of a social graph by also incorporating information on interests. When the Extended Social Graph is built, state-of-the-art techniques are applied over it in order to discover communities. Once these social communities are found, statistical techniques will look for relevant patterns common to each of those, in such a way that each cluster of users is characterised by a set of common features. The resulting knowledge will be used to develop and evaluate a social recommender system, which aims at suggesting users in a social network with possible friends or interests.

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Section: Community Detectionmentioning

confidence: 99%

Beyond social graphs: mining patterns underlying social interactions

Baldominos

Calle

Cuadra

2016

Pattern Anal Applic

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“…In the attempt of comparing the properties of the global network with those of ego-networks, recent studies found that local structural attributes are characterized by local biases [27] that are direct implications of the friendship paradox [21]. Multiple techniques have been proposed to discover social or topical sub-groups within ego-networks [53,37,40,13], but with little attention to the dynamics of their growth. Kikas et al conducted one of the few studies touching upon the the temporal evolution of ego-networks, using a dataset of Skype contacts [30].…”

Section: Related Workmentioning

confidence: 99%

Evolution of Ego-networks in Social Media with Link Recommendations

Aiello

Barbieri²

2017

Proceedings of the Tenth ACM International Conference on Web Search and Data Mining

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Ego-networks are fundamental structures in social graphs, yet the process of their evolution is still widely unexplored. In an online context, a key question is how link recommender systems may skew the growth of these networks, possibly restraining diversity. To shed light on this matter, we analyze the complete temporal evolution of 170M ego-networks extracted from Flickr and Tumblr, comparing links that are created spontaneously with those that have been algorithmically recommended. We find that the evolution of ego-networks is bursty, community-driven, and characterized by subsequent phases of explosive diameter increase, slight shrinking, and stabilization. Recommendations favor popular and well-connected nodes, limiting the diameter expansion. With a matching experiment aimed at detecting causal relationships from observational data, we find that the bias introduced by the recommendations fosters global diversity in the process of neighbor selection. Last, with two link prediction experiments, we show how insights from our analysis can be used to improve the effectiveness of social recommender systems.Comment: Proceedings of the 10th ACM International Conference on Web Search and Data Mining (WSDM 2017), Cambridge, UK. 10 pages, 16 figures, 1 tabl

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“…This method is limited to topological aspects. Similarly, a recent study, (Biswas & Biswas, 2015), is focused on ego centric community detection in network data and it is emphasized on structural aspect alone, i.e., reachability and isolability. Hanhe Lin (Lin, 2010) has identified hidden relationships in the email network to highlight mutual private communication.…”

Section: Related Workmentioning

confidence: 99%

A multi-user perspective for personalized email communities

Nawaz

Khan

Lee

2016

Expert Systems with Applications

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Email classification and prioritization expert systems have the potential to automatically group emails and users as communities based on their communication patterns, which is one of the most tedious tasks. The exchange of emails among users along with the time and content information determine the pattern of communication. The intelligent systems extract these patterns from an email corpus of single or all users and are limited to statistical analysis. However, the email information revealed in those methods is either constricted or widespread, i.e. single or all users respectively, which limits the usability of the resultant communities. In contrast to extreme views of the email information, we relax the aforementioned restrictions by considering a subset of all users as multiuser information in an incremental way to extend the personalization concept. Accordingly, we propose a multi-user personalized email community detection method to discover the groupings of email users based on their structural and semantic intimacy. We construct a social graph using multi-user personalized emails. Subsequently, the social graph is uniquely leveraged with expedient attributes, such as semantics, to identify user communities through collaborative similarity measure. The multi-user personalized communities, which are evaluated through different quality measures, enable the email systems to filter spam or malicious emails and suggest contacts while composing emails. The experimental results over two randomly selected users from email network, as constrained information, unveil partial interaction among 80% email users with 14% search space reduction where we notice 25% improvement in the clustering coefficient.

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Investigating community structure in perspective of ego network

Cited by 45 publications

References 59 publications

Beyond social graphs: mining patterns underlying social interactions

Beyond social graphs: mining patterns underlying social interactions

Evolution of Ego-networks in Social Media with Link Recommendations

A multi-user perspective for personalized email communities

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