Results 1  10
of
10
Clustering and Community Detection in Directed Networks: A Survey
, 2013
"... Networks (or graphs) appear as dominant structures in diverse domains, including sociology, biology, neuroscience and computer science. In most of the aforementioned cases graphs are directed – in the sense that there is directionality on the edges, making the semantics of the edges non symmetric as ..."
Abstract

Cited by 10 (0 self)
 Add to MetaCart
Networks (or graphs) appear as dominant structures in diverse domains, including sociology, biology, neuroscience and computer science. In most of the aforementioned cases graphs are directed – in the sense that there is directionality on the edges, making the semantics of the edges non symmetric as the source node transmits some property to the target one but not vice versa. An interesting feature that real networks present is the clustering or community structure property, under which the graph topology is organized into modules commonly called communities or clusters. The essence here is that nodes of the same community are highly similar while on the contrary, nodes across communities present low similarity. Revealing the underlying community structure of directed complex networks has become a crucial and interdisciplinary topic with a plethora of relevant application domains. Therefore, naturally there is a recent wealth of research production in the area of mining directed graphs – with clustering being the primary method sought and the primary tool for community detection and evaluation. The goal of this paper is to offer an indepth comparative review of the methods presented so far for clustering directed networks along with the relevant necessary methodological background and also related applications. The survey commences by offering a concise review of the fundamental concepts and methodological base on which graph clustering algorithms
Social Influence Based Clustering of Heterogeneous Information Networks
"... Social networks continue to grow in size and the type of information hosted. We witness a growing interest in clustering a social network of people based on both their social relationships and their participations in activity based information networks. In this paper, we present a social influence ..."
Abstract

Cited by 7 (3 self)
 Add to MetaCart
(Show Context)
Social networks continue to grow in size and the type of information hosted. We witness a growing interest in clustering a social network of people based on both their social relationships and their participations in activity based information networks. In this paper, we present a social influence based clustering framework for analyzing heterogeneous information networks with three unique features. First, we introduce a novel social influence based vertex similarity metric in terms of both selfinfluence similarity and coinfluence similarity. We compute selfinfluence and coinfluence based similarity based on social graph and its associated activity graphs and influence graphs respectively. Second, we compute the combined social influence based similarity between each pair of vertices by unifying the selfsimilarity and multiple coinfluence similarity scores through a weight function with an iterative update method. Third, we design an iterative learning algorithm, SICluster, to dynamically refine the K clusters by continuously quantifying and adjusting the weights on selfinfluence similarity and on multiple coinfluence similarity scores towards the clustering convergence. To make SICluster converge fast, we transformed a sophisticated nonlinear fractional programming problem of multiple weights into a straightforward nonlinear parametric programming problem of single variable. Our experiment results show that SICluster not only achieves a better balance between selfinfluence and coinfluence similarities but also scales extremely well for large graph clustering.
Scalable and MemoryEfficient Clustering of LargeScale Social Networks
"... Abstract—Clustering of social networks is an important task for their analysis; however, most existing algorithms do not scale to the massive size of today’s social networks. A popular class of graph clustering algorithms for largescale networks, such as PMetis, KMetis and Graclus, is based on a mu ..."
Abstract

Cited by 3 (3 self)
 Add to MetaCart
(Show Context)
Abstract—Clustering of social networks is an important task for their analysis; however, most existing algorithms do not scale to the massive size of today’s social networks. A popular class of graph clustering algorithms for largescale networks, such as PMetis, KMetis and Graclus, is based on a multilevel framework. Generally, these multilevel algorithms work reasonably well on networks with a few million vertices. However, when the network size increases to the scale of 10 million vertices or greater, the performance of these algorithms rapidly degrades. Furthermore, an inherent property of social networks, the power law degree distribution, makes these algorithms infeasible to apply to largescale social networks. In this paper, we propose a scalable and memoryefficient clustering algorithm for largescale social networks. We name our algorithm GEM, by mixing two key concepts of the algorithm, Graph Extraction and weighted kernel kMeans. GEM efficiently extracts a good skeleton graph from the original graph, and propagates the clustering result of the extracted graph to the rest of the network. Experimental results show that GEM produces clusters of quality comparable to or better than existing stateoftheart graph clustering algorithms, while it is much faster and consumes much less memory. Furthermore, the parallel implementation of GEM, called PGEM, not only produces higher quality of clusters but also achieves much better scalability than most current parallel graph clustering algorithms. Keywordsclustering; social networks; graph clustering; scalable computing; graph partitioning; kernel kmeans; I.
Contentbased Modeling and Prediction of Information Dissemination
"... Abstract—Social and communication networks across the world generate vast amounts of graphlike data each day. The modeling and prediction of how these communication structures evolve can be highly useful for many applications. Previous research in this area has focused largely on using past graph s ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
(Show Context)
Abstract—Social and communication networks across the world generate vast amounts of graphlike data each day. The modeling and prediction of how these communication structures evolve can be highly useful for many applications. Previous research in this area has focused largely on using past graph structure to predict future links. However, a useful observation is that many graph datasets have additional information associated with them beyond just their graph structure. In particular, communication graphs (such as email, twitter, blog graphs, etc.) have information content associated with their graph edges. In this paper we examine the link between information content and graph structure, proposing a new graph modeling approach, GCModel, which combines both. We then apply this model to multiple real world communication graphs, demonstrating that the built models can be used effectively to predict future graph structure and information flow. On average, GCModel’s top predictions covered 19 % more of the actual future graph communication structure when compared to other previously introduced algorithms, far outperforming multiple link prediction methods and several naive approaches. I.
Community detection in largescale networks: a survey and empirical evaluation. WIREs Comput Stat
, 2014
"... Community detection is a common problem in graph data analytics that consists of finding groups of densely connected nodes with few connections to nodes outside of the group. In particular, identifying communities in largescale networks is an important task in many scientific domains. In this revi ..."
Abstract

Cited by 2 (1 self)
 Add to MetaCart
Community detection is a common problem in graph data analytics that consists of finding groups of densely connected nodes with few connections to nodes outside of the group. In particular, identifying communities in largescale networks is an important task in many scientific domains. In this review, we evaluated eight stateoftheart and five traditional algorithms for overlapping and disjoint community detection on largescale realworld networks with known groundtruth communities. These 13 algorithms were empirically compared using goodness metrics that measure the structural properties of the identified communities, as well as performance metrics that evaluate these communities against the groundtruth. Our results show that these two types of metrics are not equivalent. That is, an algorithm may perform well in terms of goodness metrics, but poorly in terms of performance metrics, or vice versa.
A Divide and Conquer Framework for Distributed Graph Clustering
"... Graph clustering is about identifying clusters of closely connected nodes, and is a fundamental technique of data analysis with many applications including community detection, VLSI network partitioning, collaborative filtering, etc. In order to improve the scalability of existing graph clustering ..."
Abstract
 Add to MetaCart
(Show Context)
Graph clustering is about identifying clusters of closely connected nodes, and is a fundamental technique of data analysis with many applications including community detection, VLSI network partitioning, collaborative filtering, etc. In order to improve the scalability of existing graph clustering algorithms, we propose a novel divide and conquer framework for graph clustering, and establish theoretical guarantees of exact recovery of the clusters. One additional advantage of the proposed framework is that it can identify small clusters – the size of the smallest cluster can be of size o( p n), in contrast to Ω(
Integrating Vertexcentric Clustering with Edgecentric Clustering for Meta Path Graph Analysis
"... Meta paths are good mechanisms to improve the quality of graph analysis on heterogeneous information networks. This paper presents a meta path graph clustering framework, VEPathCluster, that combines meta path vertexcentric clustering with meta path edgecentric clustering for improving the cluste ..."
Abstract
 Add to MetaCart
(Show Context)
Meta paths are good mechanisms to improve the quality of graph analysis on heterogeneous information networks. This paper presents a meta path graph clustering framework, VEPathCluster, that combines meta path vertexcentric clustering with meta path edgecentric clustering for improving the clustering quality of heterogeneous networks. First, we propose an edgecentric path graph model to capture the metapath dependencies between pairwise path edges. We model a heterogeneous network containing M types of meta paths as M vertexcentric path graphs and M edgecentric path graphs. Second, we propose a clusteringbased multigraph model to capture the finegrained clusteringbased relationships between pairwise vertices and between pairwise path edges. We perform clustering analysis on both a unified vertexcentric path graph and each edgecentric path graph to generate vertex clustering and edge clusterings of the original heterogeneous network respectively. Third, a reinforcement algorithm is provided to tightly integrate vertexcentric clustering and edgecentric clustering by mutually enhancing each other. Finally, an iterative learning strategy is presented to dynamically refine both vertexcentric clustering and edgecentric clustering by continuously learning the contributions and adjusting the weights of different path graphs.
3 Maximizing Acceptance Probability for Active Friending in OnLine Social Networks
"... ar ..."
(Show Context)
Maximizing Acceptance Probability for Active Friending in Online Social Networks
"... Friending recommendation has successfully contributed to the explosive growth of online social networks. Most friending recommendation services today aim to support passive friending, where a user passively selects friending targets from the recommended candidates. In this paper, we advocate a rec ..."
Abstract
 Add to MetaCart
(Show Context)
Friending recommendation has successfully contributed to the explosive growth of online social networks. Most friending recommendation services today aim to support passive friending, where a user passively selects friending targets from the recommended candidates. In this paper, we advocate a recommendation support for active friending, where a user actively specifies a friending target. To the best of our knowledge, a recommendation designed to provide guidance for a user to systematically approach his friending target has not been explored for existing online social networking services. To maximize the probability that the friending target would accept an invitation from the user, we formulate a new optimization problem, namely, Acceptance Probability Maximization (APM), and develop a polynomial time algorithm,