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Community detection in large-scale 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 large-scale networks is an important task in many scientific domains. In this revi ..."
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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 large-scale networks is an important task in many scientific domains. In this review, we evaluated eight state-of-the-art and five traditional algorithms for overlapping and disjoint community detection on large-scale real-world networks with known ground-truth 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 ground-truth. 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.
Tensor Spectral Clustering for Partitioning Higher-order Network Structures
"... Spectral graph theory-based methods represent an important class of tools for studying the structure of networks. Spectral methods are based on a first-order Markov chain derived from a random walk on the graph and thus they cannot take advantage of important higher-order network substruc-tures such ..."
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Spectral graph theory-based methods represent an important class of tools for studying the structure of networks. Spectral methods are based on a first-order Markov chain derived from a random walk on the graph and thus they cannot take advantage of important higher-order network substruc-tures such as triangles, cycles, and feed-forward loops. Here we propose a Tensor Spectral Clustering (TSC) algorithm that allows for modeling higher-order network structures in a graph partitioning framework. Our TSC algorithm allows the user to specify which higher-order network structures (cycles, feed-forward loops, etc.) should be preserved by the network clustering. Higher-order network structures of in-terest are represented using a tensor, which we then partition by developing a multilinear spectral method. Our framework can be applied to discovering layered flows in networks as well as graph anomaly detection, which we illustrate on synthetic networks. In directed networks, a higher-order struc-ture of particular interest is the directed 3-cycle, which captures feedback loops in networks. We demonstrate that our TSC algorithm produces large partitions that cut fewer directed 3-cycles than standard spectral clustering algorithms.
Detecting Cohesive and 2-mode Communities in Directed and Undirected Networks
"... Networks are a general language for representing relational infor-mation among objects. An effective way to model, reason about, and summarize networks, is to discover sets of nodes with com-mon connectivity patterns. Such sets are commonly referred to as network communities. Research on network com ..."
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Networks are a general language for representing relational infor-mation among objects. An effective way to model, reason about, and summarize networks, is to discover sets of nodes with com-mon connectivity patterns. Such sets are commonly referred to as network communities. Research on network community detection has predominantly focused on identifying communities of densely connected nodes in undirected networks. In this paper we develop a novel overlapping community de-tection method that scales to networks of millions of nodes and edges and advances research along two dimensions: the connec-tivity structure of communities, and the use of edge directedness for community detection. First, we extend traditional definitions of network communities by building on the observation that nodes can be densely interlinked in two different ways: In cohesive commu-nities nodes link to each other, while in 2-mode communities nodes link in a bipartite fashion, where links predominate between the two partitions rather than inside them. Our method successfully detects both 2-mode as well as cohesive communities, that may also over-lap or be hierarchically nested. Second, while most existing com-munity detection methods treat directed edges as though they were undirected, our method accounts for edge directions and is able to identify novel and meaningful community structures in both di-rected and undirected networks, using data from social, biological, and ecological domains.
Innovation as Creative Response. Determinants of Innovation in the Swedish Manufacturing Industry, 1970-2007 Taalbi, Josef Published: 2014-01-01
, 2014
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Measuring the Statistical Significance of Local Connections in Directed Networks
"... Partitioning a network into different communities so that vertices of the same community share meaningful density- and pattern-based similarities is an important area of research in the field of network science. For directed networks identifying communities turns out to be especially challenging sin ..."
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Partitioning a network into different communities so that vertices of the same community share meaningful density- and pattern-based similarities is an important area of research in the field of network science. For directed networks identifying communities turns out to be especially challenging since the directed nature of the edges makes it difficult to evaluate and interpret the significance of a candidate community. In this paper, we consider the strength of connections from a single vertex to a prespecified collection of vertices in directed networks. We propose a methodology to measure the statistical significance of these connections through the use of p-values derived from a directed configuration null model. We derive the asymptotic distribution of the number of edges between a vertex and a community under the null model and show how to calculate p-values using this reference distribution. Using both simulated and real data sets we show that these conditionally based p-values can provide novel insights into the local structure of directed networks. 1
Considerations about multistep community detection
"... Abstract. The problem and implications of community detection in networks have raised a huge attention, for its important applications in both natural and social sciences. A number of algorithms has been developed to solve this problem, addressing either speed optimization or the quality of the part ..."
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Abstract. The problem and implications of community detection in networks have raised a huge attention, for its important applications in both natural and social sciences. A number of algorithms has been developed to solve this problem, addressing either speed optimization or the quality of the partitions calculated. In this paper we propose a multi-step procedure bridging the fastest, but less accurate algorithms (coarse clustering), with the slowest, most effective ones (refinement). By adopting heuristic ranking of the nodes, and classifying a fraction of them as ‘critical’, a refinement step can be restricted to this subset of the net-work, thus saving computational time. Preliminary numerical results are discussed, showing improvement of the final partition.
RESEARCH ARTICLE Evolution of Cooperation Patterns in Psoriasis Research: Co-Authorship Network Analysis of Papers in Medline (1942–2013)
, 1371
"... Background Although researchers have worked in collaboration since the origins of modern science and the publication of the first scientific journals in the eighteenth century, this phenomenon has acquired exceptional importance in the last several decades. Since the mid-twentieth cen-tury, new know ..."
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Background Although researchers have worked in collaboration since the origins of modern science and the publication of the first scientific journals in the eighteenth century, this phenomenon has acquired exceptional importance in the last several decades. Since the mid-twentieth cen-tury, new knowledge has been generated from within an ever-growing network of investiga-tors, working cooperatively in research groups across countries and institutions. Cooperation is a crucial determinant of academic success. Objective The aim of the present paper is to analyze the evolution of scientific collaboration at the micro level, with regard to the scientific production generated on psoriasis research. Methods A bibliographic search in the Medline database containing the MeSH terms “psoriasis ” or “psoriatic arthritis ” was carried out. The search results were limited to articles, reviews and letters. After identifying the co-authorships of documents on psoriasis indexed in the Med-
A Novel Algorithm for Community Detection and Influence Ranking in Social Networks
"... Abstract—Community detection and influence analysis are significant notions in social networks. We exploit the implicit knowledge of influence-based connectivity and proximity encoded in the network topology, and propose a novel algorithm for both community detection and influence ranking. Using a n ..."
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Abstract—Community detection and influence analysis are significant notions in social networks. We exploit the implicit knowledge of influence-based connectivity and proximity encoded in the network topology, and propose a novel algorithm for both community detection and influence ranking. Using a new influence cascade model, the algorithm generates an influence vector for each node, which captures in detail how the node’s influence is distributed through the network. Similarity in this influence space defines a new, meaningful and refined connectivity measure for the closeness of any pair of nodes. Our approach not only differentiates the influence ranking but also effectively finds communities in both undirected and directed networks, and incorporates these two important tasks into one integrated frame-work. We demonstrate its superior performance with extensive tests on a set of real-world networks and synthetic benchmarks. I.
RESEARCH ARTICLE A Network of Networks Perspective on Global Trade
"... Mutually intertwined supply chains in contemporary economy result in a complex network of trade relationships with a highly non-trivial topology that varies with time. In order to under-stand the complex interrelationships among different countries and economic sectors, as well as their dynamics, a ..."
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Mutually intertwined supply chains in contemporary economy result in a complex network of trade relationships with a highly non-trivial topology that varies with time. In order to under-stand the complex interrelationships among different countries and economic sectors, as well as their dynamics, a holistic view on the underlying structural properties of this network is necessary. This study employs multi-regional input-output data to decompose 186 national economies into 26 industry sectors and utilizes the approach of interdependent net-works to analyze the substructure of the resulting international trade network for the years 1990–2011. The partition of the network into national economies is observed to be compati-ble with the notion of communities in the sense of complex network theory. By studying internal versus cross-subgraph contributions to established complex network metrics, new insights into the architecture of global trade are obtained, which allow to identify key ele-ments of global economy. Specifically, financial services and business activities dominate domestic trade whereas electrical and machinery industries dominate foreign trade. In order to further specify each national sector’s role individually, (cross-)clustering coefficients and
