Overlapping community detection in massive social networks

Massive social networks have become increasingly popular in recent years. Community detection is one of the most important techniques for the analysis of such complex networks. A community is a set of cohesive vertices that has more connections inside the set than outside. In many social and information networks, these communities naturally overlap. For instance, in a social network, each vertex in a graph corresponds to an individual who usually participates in multiple communities. In this thesis, we propose scalable overlapping community detection algorithms that effectively identify high quality overlapping communities in various real-world networks. We first develop an efficient overlapping community detection algorithm using a seed set expansion approach. The key idea of this algorithm is to find good seeds and then greedily expand these seeds using a personalized PageRank clustering scheme. Experimental results show that our algorithm significantly outperforms other state-of-the-art overlapping community detection methods in terms of run time, cohesiveness of communities, and ground-truth accuracy. To develop more principled methods, we formulate the overlapping community detection problem as a non-exhaustive, overlapping graph clustering problem where clusters are allowed to overlap with each other, and some nodes are allowed to be outside of any cluster. To tackle this non-exhaustive, overlapping clustering problem, we propose a simple and intuitive objective function that captures the issues of overlap and non-exhaustiveness in a unified manner. To optimize the objective, we develop not only fast iterative algorithms but also more sophisticated algorithms using a low-rank semidefinite programming technique. Our experimental results show that the new objective and the algorithms are effective in finding ground-truth clusterings that have varied overlap and non-exhaustiveness. We extend our non-exhaustive, overlapping clustering techniques to co-clustering where the goal is to simultaneously identify a clustering of the rows as well as the columns of a data matrix. As an example application, consider recommender systems where users have ratings on items. This can be represented by a bipartite graph where users and items are denoted by two different types of nodes, and the ratings are denoted by weighted edges between the users and the items. In this case, co-clustering would be a simultaneous clustering of users and items. We propose a new co-clustering objective function and an efficient co-clustering algorithm that is able to identify overlapping clusters as well as outliers on both types of the nodes in the bipartite graph. We show that our co-clustering algorithm is able to effectively capture the underlying co-clustering structure of the data, which results in boosting the performance of a standard one-dimensional clustering. Finally, we study the design of parallel data-driven algorithms, which enables us to further increase the scalability of our overlapping community detection algorithms. Using PageRank as a model problem, we look at three algorithm design axes: work activation, data access pattern, and scheduling. We investigate the impact of different algorithm design choices. Using these design axes, we design and test a variety of PageRank implementations finding that data-driven, push-based algorithms are able to achieve a significantly superior scalability than standard PageRank implementations. The design choices affect both single-threaded performance as well as parallel scalability. The lessons learned from this study not only guide efficient implementations of many graph mining algorithms but also provide a framework for designing new scalable algorithms, especially for large-scale community detection.Computer Science

Online Tensor Methods for Learning Latent Variable Models

We introduce an online tensor decomposition based approach for two latent variable modeling problems namely, (1) community detection, in which we learn the latent communities that the social actors in social networks belong to, and (2) topic modeling, in which we infer hidden topics of text articles. We consider decomposition of moment tensors using stochastic gradient descent. We conduct optimization of multilinear operations in SGD and avoid directly forming the tensors, to save computational and storage costs. We present optimized algorithm in two platforms. Our GPU-based implementation exploits the parallelism of SIMD architectures to allow for maximum speed-up by a careful optimization of storage and data transfer, whereas our CPU-based implementation uses efficient sparse matrix computations and is suitable for large sparse datasets. For the community detection problem, we demonstrate accuracy and computational efficiency on Facebook, Yelp and DBLP datasets, and for the topic modeling problem, we also demonstrate good performance on the New York Times dataset. We compare our results to the state-of-the-art algorithms such as the variational method, and report a gain of accuracy and a gain of several orders of magnitude in the execution time.Comment: JMLR 201

Scalable and interpretable product recommendations via overlapping co-clustering

We consider the problem of generating interpretable recommendations by identifying overlapping co-clusters of clients and products, based only on positive or implicit feedback. Our approach is applicable on very large datasets because it exhibits almost linear complexity in the input examples and the number of co-clusters. We show, both on real industrial data and on publicly available datasets, that the recommendation accuracy of our algorithm is competitive to that of state-of-art matrix factorization techniques. In addition, our technique has the advantage of offering recommendations that are textually and visually interpretable. Finally, we examine how to implement our technique efficiently on Graphical Processing Units (GPUs).Comment: In IEEE International Conference on Data Engineering (ICDE) 201

VoG: Summarizing and Understanding Large Graphs

How can we succinctly describe a million-node graph with a few simple sentences? How can we measure the "importance" of a set of discovered subgraphs in a large graph? These are exactly the problems we focus on. Our main ideas are to construct a "vocabulary" of subgraph-types that often occur in real graphs (e.g., stars, cliques, chains), and from a set of subgraphs, find the most succinct description of a graph in terms of this vocabulary. We measure success in a well-founded way by means of the Minimum Description Length (MDL) principle: a subgraph is included in the summary if it decreases the total description length of the graph. Our contributions are three-fold: (a) formulation: we provide a principled encoding scheme to choose vocabulary subgraphs; (b) algorithm: we develop \method, an efficient method to minimize the description cost, and (c) applicability: we report experimental results on multi-million-edge real graphs, including Flickr and the Notre Dame web graph.Comment: SIAM International Conference on Data Mining (SDM) 201

An Ensemble Framework for Detecting Community Changes in Dynamic Networks

Dynamic networks, especially those representing social networks, undergo constant evolution of their community structure over time. Nodes can migrate between different communities, communities can split into multiple new communities, communities can merge together, etc. In order to represent dynamic networks with evolving communities it is essential to use a dynamic model rather than a static one. Here we use a dynamic stochastic block model where the underlying block model is different at different times. In order to represent the structural changes expressed by this dynamic model the network will be split into discrete time segments and a clustering algorithm will assign block memberships for each segment. In this paper we show that using an ensemble of clustering assignments accommodates for the variance in scalable clustering algorithms and produces superior results in terms of pairwise-precision and pairwise-recall. We also demonstrate that the dynamic clustering produced by the ensemble can be visualized as a flowchart which encapsulates the community evolution succinctly.Comment: 6 pages, under submission to HPEC Graph Challeng

{VoG}: {Summarizing} and Understanding Large Graphs

How can we succinctly describe a million-node graph with a few simple sentences? How can we measure the "importance" of a set of discovered subgraphs in a large graph? These are exactly the problems we focus on. Our main ideas are to construct a "vocabulary" of subgraph-types that often occur in real graphs (e.g., stars, cliques, chains), and from a set of subgraphs, find the most succinct description of a graph in terms of this vocabulary. We measure success in a well-founded way by means of the Minimum Description Length (MDL) principle: a subgraph is included in the summary if it decreases the total description length of the graph. Our contributions are three-fold: (a) formulation: we provide a principled encoding scheme to choose vocabulary subgraphs; (b) algorithm: we develop \method, an efficient method to minimize the description cost, and (c) applicability: we report experimental results on multi-million-edge real graphs, including Flickr and the Notre Dame web graph

DHLP 1&2: Giraph based distributed label propagation algorithms on heterogeneous drug-related networks

Background and Objective: Heterogeneous complex networks are large graphs consisting of different types of nodes and edges. The knowledge extraction from these networks is complicated. Moreover, the scale of these networks is steadily increasing. Thus, scalable methods are required. Methods: In this paper, two distributed label propagation algorithms for heterogeneous networks, namely DHLP-1 and DHLP-2 have been introduced. Biological networks are one type of the heterogeneous complex networks. As a case study, we have measured the efficiency of our proposed DHLP-1 and DHLP-2 algorithms on a biological network consisting of drugs, diseases, and targets. The subject we have studied in this network is drug repositioning but our algorithms can be used as general methods for heterogeneous networks other than the biological network. Results: We compared the proposed algorithms with similar non-distributed versions of them namely MINProp and Heter-LP. The experiments revealed the good performance of the algorithms in terms of running time and accuracy.Comment: Source code available for Apache Giraph on Hadoo