Most social network analysis works at the level of interactions between users. But the vast growth in size and complexity of social networks enables us to examine interactions at larger scale. In this work we use a dataset of 76M submissions to the social network Reddit, which is organized into distinct sub-communities called subreddits. We measure the similarity between entire subreddits both in terms of user similarity and topical similarity. Our goal is to find community pairs with similar userbases, but dissimilar content; we refer to this type of relationship as a "latent interest." Detection of latent interests not only provides a perspective on individual users as they shift between roles (student, sports fan, political activist) but also gives insight into the dynamics of Reddit as a whole. Latent interest detection also has potential applications for recommendation systems and for researchers examining community evolution.
There also exist several topic models for uncovering latent network structure that take content and social structure into account. For instance, Topic-link LDA @cite_6 , Pairwise Link-LDA @cite_8 , and Relational Topic Models @cite_15 jointly model social structures and user content. Reddit has been specifically examined recently using backbone networks @cite_10 but without looking deeply at textual content.
@cite_15 @cite_10 @cite_6 @cite_8
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This article studies the problem of latent community topic analysis in text-associated graphs. With the development of social media, a lot of user-generated content is available with user networks. Along with rich information in networks, user graphs can be extended with text information associated with nodes. Topic modeling is a classic problem in text mining and it is interesting to discover the latent topics in text-associated graphs. Different from traditional topic modeling methods considering links, we incorporate community discovery into topic analysis in text-associated graphs to guarantee the topical coherence in the communities so that users in the same community are closely linked to each other and share common latent topics. We handle topic modeling and community discovery in the same framework. In our model we separate the concepts of community and topic, so one community can correspond to multiple topics and multiple communities can share the same topic. We compare different methods and perform extensive experiments on two real datasets. The results confirm our hypothesis that topics could help understand community structure, while community structure could help model topics. 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-association). On average, LDA-G outperforms IRM by 15 and Cross-association by 25 (in terms of area under the ROC curve). Furthermore, we demonstrate that LDA-G can discover useful qualitative information. As the availability and importance of relational data—such as the friendships summarized on a social networking website—increases, it becomes increasingly important to have good models for such data. The kinds of latent structure that have been considered for use in predicting links in such networks have been relatively limited. In particular, the machine learning community has focused on latent class models, adapting Bayesian nonparametric methods to jointly infer how many latent classes there are while learning which entities belong to each class. We pursue a similar approach with a richer kind of latent variable—latent features—using a Bayesian nonparametric approach to simultaneously infer the number of features at the same time we learn which entities have each feature. Our model combines these inferred features with known covariates in order to perform link prediction. We demonstrate that the greater expressiveness of this approach allows us to improve performance on three datasets. Community discovery has drawn significant research interests among researchers from many disciplines for its increasing application in multiple, disparate areas, including computer science, biology, social science and so on. This paper describes an LDA(latent Dirichlet Allocation)-based hierarchical Bayesian algorithm, namely SSN-LDA (simple social network LDA). In SSN-LDA, communities are modeled as latent variables in the graphical model and defined as distributions over the social actor space. The advantage of SSN-LDA is that it only requires topological information as input. This model is evaluated on two research collaborative networkst: CtteSeer and NanoSCI. The experimental results demonstrate that this approach is promising for discovering community structures in large-scale networks.