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14
Centralities: Capturing the Fuzzy Notion of Importance in Social Graphs
 SNS
, 2009
"... The increase of interest in the analysis of contemporary social networks, for both academic and economic reasons, has highlighted the inherent difficulties in handling large and complex structures. Among the tools provided by researchers for network analysis, the centrality notion, capturing the imp ..."
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The increase of interest in the analysis of contemporary social networks, for both academic and economic reasons, has highlighted the inherent difficulties in handling large and complex structures. Among the tools provided by researchers for network analysis, the centrality notion, capturing the importance of individuals in a graph, is of particular interest. Despite many definitions and implementations of centrality, no clear advantage is given to a particular paradigm for the study of social network characteristics. In this paper we review, compare and highlight the strengths of different definitions of centralities in contemporary social networks. 1.
Online estimating the k central nodes of a network
 In Proc. of the IEEE Network Science Workshop (NSW
, 2011
"... Estimating the most influential nodes in a network is a fundamental problem in network analysis. Influential nodes may be important spreaders of diseases in biological networks, key actors in terrorist networks, or marketing targets in social ..."
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Cited by 3 (2 self)
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Estimating the most influential nodes in a network is a fundamental problem in network analysis. Influential nodes may be important spreaders of diseases in biological networks, key actors in terrorist networks, or marketing targets in social
Incremental Algorithms for Network Management and Analysis based on Closeness Centrality
, 1303
"... Analyzing networks requires complex algorithms to extract meaningful information. Centrality metrics have shown to be correlated with the importance and loads of the nodes in network traffic. Here, we are interested in the problem of centralitybased network management. The problem has many applicat ..."
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Analyzing networks requires complex algorithms to extract meaningful information. Centrality metrics have shown to be correlated with the importance and loads of the nodes in network traffic. Here, we are interested in the problem of centralitybased network management. The problem has many applications such as verifying the robustness of the networks and controlling or improving the entity dissemination. It can be defined as finding a small set of topological network modifications which yield a desired closeness centrality configuration. As a fundamental building block to tackle that problem, we propose incremental algorithms which efficiently update the closeness centrality values upon changes in network topology, i.e., edge insertions and deletions. Our algorithms are proven to be efficient on many reallife networks, especially on smallworld networks, which have a small diameter and a spikeshaped shortest distance distribution. In addition to closeness centrality, they can also be a great arsenal for the shortestpathbased management and analysis of the networks. We experimentally validate the efficiency of our algorithms on large networks and show that they update the closeness centrality values of the temporal DBLPcoauthorship network of 1.2 million users 460 times faster than it would take to compute them from scratch. To the best of our knowledge, this is the first work which can yield practical largescale network management based on closeness centrality values.
kCentralities: Local Approximations of Global Measures Based on Shortest Paths
"... A lot of centrality measures have been developed to analyze different aspects of importance. Some of the most popular centrality measures (e.g. betweenness centrality, closeness centrality) are based on the calculation of shortest paths. This characteristic limits the applicability of these measures ..."
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A lot of centrality measures have been developed to analyze different aspects of importance. Some of the most popular centrality measures (e.g. betweenness centrality, closeness centrality) are based on the calculation of shortest paths. This characteristic limits the applicability of these measures for larger networks. In this article we elaborate on the idea of boundeddistance shortest paths calculations. We claim criteria for kcentrality measures and we introduce one algorithm for calculating both betweenness and closeness based centralities. We also present normalizations for these measures. We show that kcentrality measures are good approximations for the corresponding centrality measures by achieving a tremendous gain of calculation time and also having linear calculation complexity Θ(n) for networks with constant average degree. This allows researchers to approximate centrality measures based on shortest paths for networks with millions of nodes or with high frequency in dynamically changing networks.
STREAMER: a distributed framework for incremental closeness centrality computation
 In Proc. of IEEE Cluster
, 2013
"... Abstract—Networks are commonly used to model the traffic patterns, social interactions, or web pages. The nodes in a network do not possess the same characteristics: some nodes are naturally more connected and some nodes can be more important. Closeness centrality (CC) is a global metric that quanti ..."
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Abstract—Networks are commonly used to model the traffic patterns, social interactions, or web pages. The nodes in a network do not possess the same characteristics: some nodes are naturally more connected and some nodes can be more important. Closeness centrality (CC) is a global metric that quantifies how important is a given node in the network. When the network is dynamic and keeps changing, the relative importance of the nodes also changes. The best known algorithm to compute the CC scores makes it impractical to recompute them from scratch after each modification. In this paper, we propose STREAMER, a distributed memory framework for incrementally maintaining the closeness centrality scores of a network upon changes. It leverages pipelined and replicated parallelism and takes NUMA effects into account. It speeds up the maintenance of the CC of a real graph with 916K vertices and 4.3M edges by a factor of 497 using a 64 nodes cluster. I.
Gateway Designation for Timely Communications in Instant Mesh Networks
"... Abstract—In this paper, we explore how to effectively create and use “instant mesh networks”, i.e., wireless mesh networks that are dynamically deployed in temporary circumstances (e.g., emergency responses) – in addition to enabling coverage for internal onsite communications, such a network will ..."
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Abstract—In this paper, we explore how to effectively create and use “instant mesh networks”, i.e., wireless mesh networks that are dynamically deployed in temporary circumstances (e.g., emergency responses) – in addition to enabling coverage for internal onsite communications, such a network will support information flow into and out of the deployment site through its gateway (i.e., the mesh router that connects to the external backhaul). We study optimizing the performance of communications (specifically in terms of latency) in an instant mesh network by intelligently selecting the gateway. We demonstrate that designating the proper gateway significantly enhances the timeliness of communications with the external backhaul. We mathematically model the “gateway designation problem ” using the notion of centrality from graph theory. We propose a distributed algorithm, FACE (Fast Approximate Center Exploration), for locating the optimal gateway. FACE is an approximate algorithm that works in an efficient manner without compromising the optimality of solutions. A thorough performance evaluation shows that the gateways designated by FACE reduce latencies by up to 92 % for various types of communications, and that FACE saves transmission cost and execution time by up to 71 % in finding the gateways. Keywordsmesh network; gateway; centrality; approximation. I.
Using Spectral Clustering for Finding Students’ Using Spectral Clustering for Finding Students’ Patterns of Behavior in Social Networks Patterns of Behavior in Social Networks
"... Abstract. The high dimensionality of the data generated by social networks has been a big challenge for researchers. In order to solve the problems associated with this phenomenon, a number of methods and techniques were developed. Spectral clustering is a data mining method used in many application ..."
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Abstract. The high dimensionality of the data generated by social networks has been a big challenge for researchers. In order to solve the problems associated with this phenomenon, a number of methods and techniques were developed. Spectral clustering is a data mining method used in many applications; in this paper we used this method to find students ’ behavioral patterns performed in an elearning system. In addition, a software was introduced to allow the user (tutor or researcher) to define the data dimensions and input values to obtain appropriate graphs with behavioral pattens that meet his/her needs. Behavioral patterns were compared with students ’ study performance and evaluation with relation to their possible usage in collaborative learning. 1
Incremental Social Centrality Algorithms for Dynamic Networks
"... I would like to express my appreciation and thanks to my advisors Prof. L. Richard Carley and Prof. Kathleen M. Carley for making this thesis possible and for their guidance and support. I am very grateful for their very positive, supportive, and transparent approach. Having worked with several prof ..."
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I would like to express my appreciation and thanks to my advisors Prof. L. Richard Carley and Prof. Kathleen M. Carley for making this thesis possible and for their guidance and support. I am very grateful for their very positive, supportive, and transparent approach. Having worked with several professors, I feel happy and honored to get my Ph.D. from them. I would also like to thank my committee members Prof. Jurgen Pfeffer and Prof. Huan Liu (Arizona State) for their support and guidance. I am also very grateful to Matthew Wachs for his help and support in every step of my Ph.D. and for the encouragement he gave me to complete my degree. Last but not least, I would like to thank CASOS members for insightful discussions and feedback they gave me at various stages of my studies.
Algorithms, Performance
"... The betweenness centrality metric has always been intriguing for graph analyses and used in various applications. Yet, it is one of the most computationally expensive kernels in graph mining. In this work, we investigate a set of techniques to make the betweenness centrality computations faster on G ..."
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The betweenness centrality metric has always been intriguing for graph analyses and used in various applications. Yet, it is one of the most computationally expensive kernels in graph mining. In this work, we investigate a set of techniques to make the betweenness centrality computations faster on GPUs as well as on heterogeneous CPU/GPU architectures. Our techniques are based on virtualization of the vertices with high degree, strided access to adjacency lists, removal of the vertices with degree 1, and graph ordering. By combining these techniques within a finegrain parallelism, we reduced the computation time on GPUs significantly for a set of social networks. On CPUs, which can usually have access to a large amount of memory, we used a coarsegrain parallelism. We showed that heterogeneous computing, i.e., using both architectures at the same time, is a promising solution for betweenness centrality. Experimental results show that the proposed techniques can be a great arsenal to reduce the centrality computation time for networks. In particular, it reduces the computation time of a 234 million edges graph from more than 4 months to less than 12 days.
Incremental Algorithms for Closeness Centrality 2 IEEE BigData’13
"... citation graphs • Facebook has a billion users and a trillion connections • Twitter has more than 200 million users • Who
is
more
important
in a network?
Who
controls the flow
between
nodes? • Centrality
metrics
answer these quesAons • Closeness
Centrality
( ..."
Abstract
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citation graphs • Facebook has a billion users and a trillion connections • Twitter has more than 200 million users • Who
is
more
important
in a network?
Who
controls the flow
between
nodes? • Centrality
metrics
answer these quesAons • Closeness
Centrality
(CC)
is an intriguing
metric • How
to
handle
changes? • Incremental
algorithms
are essenAal Incremental Algorithms
for
Closeness
Centrality
3 IEEE BigData’13