Temporally Evolving Community Detection and Prediction in Content-Centric Networks

Abstract

In this work, we consider the problem of combining link, content and temporal analysis for community detection and prediction in evolving networks. Such temporal and content-rich networks occur in many real-life settings, such as bibliographic networks and question answering forums. Most of the work in the literature (that uses both content and structure) deals with static snapshots of networks, and they do not reflect the dynamic changes occurring over multiple snapshots. Incorporating dynamic changes in the communities into the analysis can also provide useful insights about the changes in the network such as the migration of authors across communities. In this work, we propose Chimera 1 , a shared factorization model that can simultaneously account for graph links, content, and temporal analysis. This approach works by extracting the latent semantic structure of the network in multidimensional form, but in a way that takes into account the temporal continuity of these embeddings. Such an approach simplifies temporal analysis of the underlying network by using the embedding as a surrogate. A consequence of this simplification is that it is also possible to use this temporal sequence of embeddings to predict future communities. We present experimental results illustrating the effectiveness of the approach.

A Distributed Bagging Ensemble Methodology for Community Prediction in Social Networks

“…Therefore, the concept of community prediction is practically a synonym for approximately detecting/predicting the subjacent community structure of any given information network based on its rudimentary network topology measures, regardless of its size. In contrast, the currently published research work [12][13][14][15][16][17][18][19], which claim to mix the community detection field with the prediction's concept, have fundamentally different incentives and objectives to the best of our knowledge.…”

“…Firstly, with effective application in viral marketing and social influence analysis, the [12,13] approaches target on the prediction of the community structure's evolution through pattern recognition and content analysis. In both cases, the a priori knowledge of the initial community structure is assumed.…”

Temporal Latent Space Modeling for Community Prediction

“…Community Prediction Baseline. To the best of our knowledge, the most related baseline to our work is a temporal content-based latent space model proposed by Appel et al [2] where shared matrix factorization has been used to embed social network dynamics and temporal content in a shared feature space followed by a traditional clustering technique, such as k-means, to identify user communities.…”

A Comparative Study of Community Detection Techniques for Large Evolving Graphs

“…For the broad selection, the initial list of 51 papers on DCD methods was used [6, 13-18, 20-23, 27-65]. It was obtained by supplementing 32 temporal trade-off algorithms [6, 13-15, 17, 21, 22, 24, 27-50] from [1] with 19 algorithms not included in the aforementioned survey [16,18,20,23,[51][52][53][54][55][56][57][58][59][60][61][62][63][64][65] that nonetheless possess interesting characteristics with regards to community and evolution extraction. Figure 1 illustrates the relevance of adding those 19 papers as it ensures the inclusion of more recent methods.…”