Results 1  10
of
269
BUBBLE Rap: Socialbased forwarding in delay tolerant networks
 in Proc. ACM MobiHoc
, 2008
"... In this paper we seek to improve our understanding of human mobility in terms of social structures, and to use these structures in the design of forwarding algorithms for Pocket Switched Networks (PSNs). Taking human mobility traces from the real world, we discover that human interaction is heteroge ..."
Abstract

Cited by 256 (30 self)
 Add to MetaCart
In this paper we seek to improve our understanding of human mobility in terms of social structures, and to use these structures in the design of forwarding algorithms for Pocket Switched Networks (PSNs). Taking human mobility traces from the real world, we discover that human interaction is heterogeneous both in terms of hubs (popular individuals) and groups or communities. We propose a social based forwarding algorithm, BUBBLE, which is shown empirically to improve the forwarding efficiency significantly compared to oblivious forwarding schemes and to PROPHET algorithm. We also show how this algorithm can be implemented in a distributed way, which demonstrates that it is applicable in the decentralised environment of PSNs.
Statistical properties of community structure in large social and information networks
"... A large body of work has been devoted to identifying community structure in networks. A community is often though of as a set of nodes that has more connections between its members than to the remainder of the network. In this paper, we characterize as a function of size the statistical and structur ..."
Abstract

Cited by 232 (14 self)
 Add to MetaCart
(Show Context)
A large body of work has been devoted to identifying community structure in networks. A community is often though of as a set of nodes that has more connections between its members than to the remainder of the network. In this paper, we characterize as a function of size the statistical and structural properties of such sets of nodes. We define the network community profile plot, which characterizes the “best ” possible community—according to the conductance measure—over a wide range of size scales, and we study over 70 large sparse realworld networks taken from a wide range of application domains. Our results suggest a significantly more refined picture of community structure in large realworld networks than has been appreciated previously. Our most striking finding is that in nearly every network dataset we examined, we observe tight but almost trivial communities at very small scales, and at larger size scales, the best possible communities gradually “blend in ” with the rest of the network and thus become less “communitylike.” This behavior is not explained, even at a qualitative level, by any of the commonlyused network generation models. Moreover, this behavior is exactly the opposite of what one would expect based on experience with and intuition from expander graphs, from graphs that are wellembeddable in a lowdimensional structure, and from small social networks that have served as testbeds of community detection algorithms. We have found, however, that a generative model, in which new edges are added via an iterative “forest fire” burning process, is able to produce graphs exhibiting a network community structure similar to our observations.
Computing communities in large networks using random walks
 J. of Graph Alg. and App. bf
, 2004
"... Dense subgraphs of sparse graphs (communities), which appear in most realworld complex networks, play an important role in many contexts. Computing them however is generally expensive. We propose here a measure of similarities between vertices based on random walks which has several important advan ..."
Abstract

Cited by 213 (2 self)
 Add to MetaCart
Dense subgraphs of sparse graphs (communities), which appear in most realworld complex networks, play an important role in many contexts. Computing them however is generally expensive. We propose here a measure of similarities between vertices based on random walks which has several important advantages: it captures well the community structure in a network, it can be computed efficiently, and it can be used in an agglomerative algorithm to compute efficiently the community structure of a network. We propose such an algorithm, called Walktrap, which runs in time O(mn 2) and space O(n 2) in the worst case, and in time O(n 2 log n) and space O(n 2) in most realworld cases (n and m are respectively the number of vertices and edges in the input graph). Extensive comparison tests show that our algorithm surpasses previously proposed ones concerning the quality of the obtained community structures and that it stands among the best ones concerning the running time.
Community structure in large networks: Natural cluster sizes and the absence of large welldefined clusters
, 2008
"... A large body of work has been devoted to defining and identifying clusters or communities in social and information networks, i.e., in graphs in which the nodes represent underlying social entities and the edges represent some sort of interaction between pairs of nodes. Most such research begins wit ..."
Abstract

Cited by 185 (17 self)
 Add to MetaCart
(Show Context)
A large body of work has been devoted to defining and identifying clusters or communities in social and information networks, i.e., in graphs in which the nodes represent underlying social entities and the edges represent some sort of interaction between pairs of nodes. Most such research begins with the premise that a community or a cluster should be thought of as a set of nodes that has more and/or better connections between its members than to the remainder of the network. In this paper, we explore from a novel perspective several questions related to identifying meaningful communities in large social and information networks, and we come to several striking conclusions. Rather than defining a procedure to extract sets of nodes from a graph and then attempt to interpret these sets as a “real ” communities, we employ approximation algorithms for the graph partitioning problem to characterize as a function of size the statistical and structural properties of partitions of graphs that could plausibly be interpreted as communities. In particular, we define the network community profile plot, which characterizes the “best ” possible community—according to the conductance measure—over a wide range of size scales. We study over 100 large realworld networks, ranging from traditional and online social networks, to technological and information networks and
Characterization of complex networks: A survey of measurements
 ADVANCES IN PHYSICS
, 2005
"... Each complex network (or class of networks) presents specific topological features which characterize its connectivity and highly influence the dynamics and function of processes executed on the network. The analysis, discrimination, and synthesis of complex networks therefore rely on the use of mea ..."
Abstract

Cited by 184 (15 self)
 Add to MetaCart
(Show Context)
Each complex network (or class of networks) presents specific topological features which characterize its connectivity and highly influence the dynamics and function of processes executed on the network. The analysis, discrimination, and synthesis of complex networks therefore rely on the use of measurements capable of expressing the most relevant topological features. This article presents a survey of such measurements. It includes general considerations about complex network characterization, a brief review of the principal models, and the presentation of the main existing measurements organized into classes. Special attention is given to relating complex network analysis with the areas of pattern recognition and feature selection, as well as on surveying some concepts and measurements from traditional graph theory which are potentially useful for complex network research. Depending on the network and the analysis task one has in mind, a specific set of features may be chosen. It is hoped that the present survey will help the
Defining and Evaluating Network Communities based on Groundtruth. Extended version
, 2012
"... Abstract—Nodes in realworld networks organize into densely linked communities where edges appear with high concentration among the members of the community. Identifying such communities of nodes has proven to be a challenging task mainly due to a plethora of definitions of a community, intractabili ..."
Abstract

Cited by 93 (4 self)
 Add to MetaCart
(Show Context)
Abstract—Nodes in realworld networks organize into densely linked communities where edges appear with high concentration among the members of the community. Identifying such communities of nodes has proven to be a challenging task mainly due to a plethora of definitions of a community, intractability of algorithms, issues with evaluation and the lack of a reliable goldstandard groundtruth. In this paper we study a set of 230 large realworld social, collaboration and information networks where nodes explicitly state their group memberships. For example, in social networks nodes explicitly join various interest based social groups. We use such groups to define a reliable and robust notion of groundtruth communities. We then propose a methodology which allows us to compare and quantitatively evaluate how different structural definitions of network communities correspond to groundtruth communities. We choose 13 commonly used structural definitions of network communities and examine their sensitivity, robustness and performance in identifying the groundtruth. We show that the 13 structural definitions are heavily correlated and naturally group into four classes. We find that two of these definitions, Conductance and Triadparticipationratio, consistently give the best performance in identifying groundtruth communities. We also investigate a task of detecting communities given a single seed node. We extend the local spectral clustering algorithm into a heuristic parameterfree community detection method that easily scales to networks with more than hundred million nodes. The proposed method achieves 30 % relative improvement over current local clustering methods. I.
Distributed community detection in delay tolerant networks
 In: Proc. of Int. Wrkshp. on Mobility in the Evolving Internet Architecture, MobiArch
, 2007
"... Community is an important attribute of Pocket Switched Networks (PSN), because mobile devices are carried by people who tend to belong to communities. We analysed community structure from mobility traces and used for forwarding algorithms [12], which shows significant impact of community. Here, we p ..."
Abstract

Cited by 91 (9 self)
 Add to MetaCart
(Show Context)
Community is an important attribute of Pocket Switched Networks (PSN), because mobile devices are carried by people who tend to belong to communities. We analysed community structure from mobility traces and used for forwarding algorithms [12], which shows significant impact of community. Here, we propose and evaluate three novel distributed community detection approaches with great potential to detect both static and temporal communities. We find that with suitable configuration of the threshold values, the distributed community detection can approximate their corresponding centralised methods up to 90 % accuracy.
Relational learning via latent social dimensions, in 'KDD '09
 Proceedings di of the 15th ACM SIGKDD international ti conference on Knowledge
, 2009
"... Social media such as blogs, Facebook, Flickr, etc., presents data in a network format rather than classical IID distribution. To address the interdependency among data instances, relational learning has been proposed, and collective inference based on network connectivity is adopted for prediction. ..."
Abstract

Cited by 87 (28 self)
 Add to MetaCart
(Show Context)
Social media such as blogs, Facebook, Flickr, etc., presents data in a network format rather than classical IID distribution. To address the interdependency among data instances, relational learning has been proposed, and collective inference based on network connectivity is adopted for prediction. However, the connections in social media are often multidimensional. An actor can connect to another actor due to different factors, e.g., alumni, colleagues, living in the same city or sharing similar interest, etc. Collective inference normally does not differentiate these connections. In this work, we propose to extract latent social dimensions based on network information first, and then utilize them as features for discriminative learning. These social dimensions describe different affiliations of social actors hidden in the network, and the subsequent discriminative learning can automatically determine which affiliations are better aligned with the class labels. Such a scheme is preferred when multiple diverse relations are associated with the same network. We conduct extensive experiments on social media data (one from a realworld blog site and the other from a popular content sharing site). Our model outperforms representative relational learning methods based on collective inference, especially when few labeled data are available. The sensitivity of this model and its connection to existing methods are also carefully examined.
An Emulator Network for
 SIMD Machine Interconnection Networks, in: Proc. 6 th annual symposium on Computer architecture
, 1979
"... Fig. 0.1. [Proposed cover figure.] The largest connected component of a network of network scientists. This network was constructed based on the coauthorship of papers listed in two wellknown review articles [13,83] and a small number of additional papers that were added manually [86]. Each node is ..."
Abstract

Cited by 84 (12 self)
 Add to MetaCart
(Show Context)
Fig. 0.1. [Proposed cover figure.] The largest connected component of a network of network scientists. This network was constructed based on the coauthorship of papers listed in two wellknown review articles [13,83] and a small number of additional papers that were added manually [86]. Each node is colored according to community membership, which was determined using a leadingeigenvector spectral method followed by KernighanLin nodeswapping steps [64, 86, 107]. To determine community placement, we used the FruchtermanReingold graph visualization [45], a forcedirected layout method that is related to maximizing a quality function known as modularity [92]. To apply this method, we treated the communities as if they were themselves the nodes of a (significantly smaller) network with connections rescaled by intercommunity links. We then used the KamadaKawaii springembedding graph visualization algorithm [62] to place the nodes of each individual community (ignoring intercommunity links) and then to rotate and flip the communities for optimal placement (including intercommunity links). We gratefully acknowledge Amanda Traud for preparing this figure. COMMUNITIES IN NETWORKS