Complex networks exist in a wide array of diverse domains, ranging from biology, sociology, and computer science. These real-world networks, while disparate in nature, often comprise of a set of loose clusters(a.k.a communities), whose members are better connected to each other than to the rest of the network. Discovering such inherent community structures can lead to deeper understanding about the networks and therefore has raised increasing interests among researchers from various disciplines. This paper describes GWN-LDA(Generic weighted network-Latent Dirichlet Allocation) model, a hierarchical Bayesian model derived from the widely-received LDA model, for discovering probabilistic community profiles in social networks. In this model, communities are modeled as latent variables and defined as distributions over the social actor space. In addition, each social actor belongs to every community with different probability. This paper also proposes two different network encoding approaches and explores the impact of these two approaches to the community discovery performance. This model is evaluated on two research collaborative networks:CiteSeer and NanoSCI. The experimental results demonstrate that this approach is promising for discovering community structures in large-scale networks.

This paper introduces LDA-G, a scalable Bayesian approach to finding latent group structures in large real-world graph data. Existing Bayesian approaches for group discovery (such as Infinite Relational Models) have only been applied to small graphs with a couple of hundred nodes. LDA-G (short for Latent Dirichlet Allocation for Graphs) utilizes a well-known topic modeling algorithm to find latent group structure. Specifically, we modify Latent Dirichlet Allocation (LDA) to operate on graph data instead of text corpora. Our modifications reflect the differences between real-world graph data and text corpora (e.g., a node’s neighbor count vs. a document’s word count). In our empirical study, we apply LDA-G to several large graphs (with thousands of nodes) from PubMed (a scientific publication repository). We compare LDA-G’s quantitative performance on link prediction with two existing approaches: one Bayesian (namely, Infinite Relational Model) and one non-Bayesian (namely, Cross-associations). On average, LDA-G outperforms IRM by 15 % and Cross-associations by 25 % (in terms of area under the ROC curve). Furthermore, we demonstrate that LDA-G can discover useful qualitative information.

Abstract—In evolving social networks, nodes join, make connections, or leave over time. In this paper, we introduce node event sequences that record activities of every node over time. Node event sequences are suitable for microscopic analysis of node behaviors in social networks. As preliminary results of taking advantage of node event sequences (as well as snapshots of networks), we study the health of the community of Nanotechnology based on the analysis of Seniority of nodes, identify the intrinsic dynamics of formation of new edges and its relation to Seniority of nodes, and the changes in the node behavior according to the nodes ’ Seniority. I.

User experience in social media involves rich interactions with the media content and other participants in the community. In order to support such communities, it is important to understand the factors that drive the users ’ engagement. In this paper we show how to define statistical models of different complexity to describe patterns of song listening in an online music community. First, we adapt the LDA model to capture music taste from listening activities across users and identify both the groups of songs associated with the specific taste and the groups of listeners who share the same taste. Second, we define a graphical model that takes into account listening sessions and captures the listening moods of users in the community. Our session model leads to groups of songs and groups of listeners with similar behavior across listening sessions and enables faster inference when compared to the LDA model. Our experiments with the data from an online media site demonstrate that the session model is better in terms of the perplexity compared to two other models: the LDA-based taste model that does not incorporate crosssession information and a baseline model that does not use latent groupings of songs.

We introduce a novel Bayesian framework for hybrid community discovery in graphs. Our framework, HCDF (short for Hybrid Community Discovery Framework), can effectively incorporate hints from a number of other community detection algorithms and produce results that outperform the constituent parts. We describe two HCDF-based approaches which are: (1) effective, in terms of link prediction performance and robustness to small perturbations in network structure; (2) consistent, in terms of effectiveness across various application domains; (3) scalable to very large graphs; and (4) nonparametric. Our extensive evaluation on a collection of diverse and large real-world graphs, with millions of links, show that our HCDF-based approaches (a) achieve up to 0.22 improvement in link prediction performance as measured by area under ROC curve (AUC), (b) never have an AUC that drops below 0.91 in the worst case, and (c) find communities that are robust to small perturbations of the network structure as defined by Variation of Information (an entropybased distance metric). 1

Real-world social networks are often hierarchical, reflecting the fact that some communities are composed of a few smaller, sub-communities. This paper describes a hierarchical Bayesian model based scheme, namely HSN-PAM (Hierarchical Social Network-Pachinko Allocation Model), for discovering probabilistic, hierarchical communities in social networks. This scheme is powered by a previously developed hierarchical Bayesian model. In this scheme, communities are classified into two categories: super-communities and regular-communities. Two different network encoding approaches are explored to evaluate this scheme on research collaborative networks, including CiteSeer and NanoSCI. The experimental results demonstrate that HSN-PAM is effective for discovering hierarchical community structures in large-scale social networks. 1

A main challenge for Web content classification is how to model the input data. This paper discusses the application of two text modeling approaches, Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA), in the Web page classification task. We report results on a comparison of these two approaches using different vocabularies consisting of links and text. Both models are evaluated using different numbers of latent topics. Finally, we evaluate a hybrid latent variable model that combines the latent topics resulting from both LSA and LDA. This new approach turns out to be superior to the basic LSA and LDA models. In our experiments with categories and pages obtained from the ODP web directory the hybrid model achieves an averaged F-measure value of 0.852 and an averaged ROC value of 0.96. 1.

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With the rise in availability and importance of graphs and networks, it has become increasingly important to have good models to describe their behavior. While much work has focused on modeling static graphs, we focus on group discovery in dynamic graphs. We adapt a dynamic extension of Latent Dirichlet Allocation to this task and demonstrate good performance on two datasets. 1

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