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Parallel Overlapping Community Detection with SLPA
"... Abstract—Social networks consist of various communities that host members sharing common characteristics. Often some members of one community are also members of other communities. Such shared membership of different communities leads to overlapping communities. Detecting such overlapping communitie ..."
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Abstract—Social networks consist of various communities that host members sharing common characteristics. Often some members of one community are also members of other communities. Such shared membership of different communities leads to overlapping communities. Detecting such overlapping communities is a challenging and computationally intensive problem. In this paper, we investigate the usability of high performance computing in the area of social networks and community detection. We present highly scalable variants of a community detection algorithm called Speaker-listener Label Propagation Algorithm (SLPA). We show that despite of irregular data dependencies in the computation, parallel computing paradigms can significantly speed up the detection of overlapping communities of social networks which is computationally expensive. We show by experiments, how various parallel computing architectures can be utilized to analyze large social network data on both shared memory machines and distributed memory machines, such as IBM Blue Gene.
Location Prediction: Communities Speak Louder than Friends
"... Humans are social animals, they interact with different com-munities of friends to conduct different activities. The lit-erature shows that human mobility is constrained by their social relations. In this paper, we investigate the social im-pact of a person’s communities on his mobility, instead of ..."
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Humans are social animals, they interact with different com-munities of friends to conduct different activities. The lit-erature shows that human mobility is constrained by their social relations. In this paper, we investigate the social im-pact of a person’s communities on his mobility, instead of all friends from his online social networks. This study can be particularly useful, as certain social behaviors are influ-enced by specific communities but not all friends. To achieve our goal, we first develop a measure to characterize a per-son’s social diversity, which we term ‘community entropy’. Through analysis of two real-life datasets, we demonstrate that a person’s mobility is influenced only by a small frac-tion of his communities and the influence depends on the social contexts of the communities. We then exploit ma-chine learning techniques to predict users ’ future movement based on their communities ’ information. Extensive experi-ments demonstrate the prediction’s effectiveness.
Overlapping Community Regularization for Rating Prediction in Social Recommender Systems∗
"... Recommender systems have become de facto tools for suggesting items that are of potential interest to users. Predicting a user’s rat-ing on an item is the fundamental recommendation task. Tradi-tional methods that generate predictions by analyzing the user-item rating matrix perform poorly when the ..."
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Recommender systems have become de facto tools for suggesting items that are of potential interest to users. Predicting a user’s rat-ing on an item is the fundamental recommendation task. Tradi-tional methods that generate predictions by analyzing the user-item rating matrix perform poorly when the matrix is sparse. Recent ap-proaches use data from social networks to improve accuracy. How-ever, most of the social-network based recommender systems only consider direct friendships and they are less effective when the tar-geted user has few social connections. In this paper, we propose two alternative models that incorporate the overlapping commu-nity regularization into the matrix factorization framework. Our empirical study on four real datasets shows that our approaches outperform the state-of-the-art algorithms in both traditional and social-network based recommender systems regarding both cold-start users and normal users. 1.
On the Consistency of the Likelihood Maximization Vertex Nomination Scheme: Bridging the Gap Between Maximum Likelihood Estimation and Graph Matching
, 2016
"... Abstract Given a graph in which a few vertices are deemed interesting a priori, the vertex nomination task is to order the remaining vertices into a nomination list such that there is a concentration of interesting vertices at the top of the list. Previous work has yielded several approaches to thi ..."
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Abstract Given a graph in which a few vertices are deemed interesting a priori, the vertex nomination task is to order the remaining vertices into a nomination list such that there is a concentration of interesting vertices at the top of the list. Previous work has yielded several approaches to this problem, with theoretical results in the setting where the graph is drawn from a stochastic block model (SBM), including a vertex nomination analogue of the Bayes optimal classifier. In this paper, we prove that maximum likelihood (ML)-based vertex nomination is consistent, in the sense that the performance of the ML-based scheme asymptotically matches that of the Bayes optimal scheme. We prove theorems of this form both when model parameters are known and unknown. Additionally, we introduce and prove consistency of a related, more scalable restricted-focus ML vertex nomination scheme. Finally, we incorporate vertex and edge features into ML-based vertex nomination and briefly explore the empirical effectiveness of this approach.
From Community Detection to Community Profiling
"... ABSTRACT Most existing community-related studies focus on detection, which aim to find the community membership for each user from user friendship links. However, membership alone, without a complete profile of what a community is and how it interacts with other communities, has limited application ..."
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ABSTRACT Most existing community-related studies focus on detection, which aim to find the community membership for each user from user friendship links. However, membership alone, without a complete profile of what a community is and how it interacts with other communities, has limited applications. This motivates us to consider systematically profiling the communities and thereby developing useful community-level applications. In this paper, we for the first time formalize the concept of community profiling. With rich user information on the network, such as user published content and user diffusion links, we characterize a community in terms of both its internal content profile and external diffusion profile. The difficulty of community profiling is often underestimated. We novelly identify three unique challenges and propose a joint Community Profiling and Detection (CPD) model to address them accordingly. We also contribute a scalable inference algorithm, which scales linearly with the data size and it is easily parallelizable. We evaluate CPD on large-scale real-world data sets, and show that it is significantly better than the state-of-the-art baselines in various tasks.
Vertex Clustering of Augmented Graph Streams
"... In this paper we propose a graph stream clustering algorithm with a unied similarity measure on both structural and attribute properties of vertices, with each attribute being treated as a vertex. Unlike others, our approach does not require an input parameter for the number of clusters, instead, it ..."
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In this paper we propose a graph stream clustering algorithm with a unied similarity measure on both structural and attribute properties of vertices, with each attribute being treated as a vertex. Unlike others, our approach does not require an input parameter for the number of clusters, instead, it dynamically creates new sketch-based clusters and periodically merges existing similar clusters. Experiments on two publicly available datasets reveal the advantages of our approach in detecting vertex clusters in the graph stream. We provide a detailed investigation into how parameters affect the algorithm performance. We also provide a quantitative evaluation and comparison with a well-known offline community detection algorithm which shows that our streaming algorithm can achieve comparable or better average cluster purity. 1
NetCodec: Community Detection from Individual Activities
"... The real social network and associated communities are often hidden under the declared friend or group lists in social networks. We usually observe the manifestation of these hidden networks and communities in the form of recurrent and time-stamped individuals ’ activities in the social network. Inf ..."
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The real social network and associated communities are often hidden under the declared friend or group lists in social networks. We usually observe the manifestation of these hidden networks and communities in the form of recurrent and time-stamped individuals ’ activities in the social network. Inferring the underlying network and finding coherent communities are therefore two key challenges in social networks analysis. In this paper, we address the following question: Could we simultaneously detect community structure and network infectivity among individuals from their ac-tivities? Based on the fact that the two characteristics intertwine and that knowing one will help better reveal-ing the other, we propose a multidimensional Hawkes process that can address them simultaneously. To this end, we parametrize the network infectivity in terms of individuals ’ participation in communities and the pop-ularity of each individual. We show that this modeling approach has many benefits, both conceptually and ex-perimentally. We utilize Bayesian variational inference to design NetCodec, an efficient inference algorithm which is verified with both synthetic and real world data sets. The experiments show that NetCodec can discover the underlying network infectivity and commu-nity structure more accurately than baseline method. 1
Density-Based Subspace Clustering in Heterogeneous Networks
"... Abstract. Many real-world data sets, like data from social media or bibliographic data, can be represented as heterogeneous networks with several vertex types. Often additional attributes are available for the vertices, such as keywords for a paper. Clustering vertices in such net-works, and analyzi ..."
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Abstract. Many real-world data sets, like data from social media or bibliographic data, can be represented as heterogeneous networks with several vertex types. Often additional attributes are available for the vertices, such as keywords for a paper. Clustering vertices in such net-works, and analyzing the complex interactions between clusters of differ-ent types, can provide useful insights into the structure of the data. To exploit the full information content of the data, clustering approaches should consider the connections in the network as well as the vertex at-tributes. We propose the density-based clustering model TCSC for the detection of clusters in heterogeneous networks that are densely con-nected in the network as well as in the attribute space. Unlike previous approaches for clustering heterogeneous networks, TCSC enables the de-tection of clusters that show similarity only in a subset of the attributes, which is more effective in the presence of a large number of attributes. 1
Evidence of Temporal Artifacts in Social Networks
"... Abstract. There has been extensive research on social networks and methods for specific tasks such as: community detection, link prediction, and tracing information cascades; and a recent emphasis on using temporal dynamics of social networks to improve method performance. The underlying models are ..."
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Abstract. There has been extensive research on social networks and methods for specific tasks such as: community detection, link prediction, and tracing information cascades; and a recent emphasis on using temporal dynamics of social networks to improve method performance. The underlying models are based on structural properties of the network, some of which we believe to be artifacts introduced from common misrepresentations of social networks. Specifically, representing a social network or series of social networks as an accumulation of network snapshots is problematic. In this paper, we use a dataset with timestamped interactions to demonstrate how cumulative graphs differ from activity-based graphs and may introduce temporal artifacts.
