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[Re] Speedup Graph Processing by Graph Ordering
2021
Zenodo
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, Danisch and Tabourier 2021
See https://perf.wiki.kernel.org. ...
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https://github.com/lecfab/rescience-gorder ReScience C 7.1 (#3) -Lécuyer, Danisch and Tabourier 2021
ReScience C 7.1 (#3) -Lécuyer ...
doi:10.5281/zenodo.4836230
fatcat:ca57qknx5fg3npchxqduwkiufe
Clique percolation method: memory efficient almost exact communities
[article]
2021
arXiv
pre-print
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Automatic detection of relevant groups of nodes in large real-world graphs, i.e. community detection, has applications in many fields and has received a lot of attention in the last twenty years. The most popular method designed to find overlapping communities (where a node can belong to several communities) is perhaps the clique percolation method (CPM). This method formalizes the notion of community as a maximal union of k-cliques that can be reached from each other through a series of
arXiv:2110.01213v1
fatcat:zhcpu5lhhndb5hhojvmggrw7r4
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... t k-cliques, where two cliques are adjacent if and only if they overlap on k-1 nodes. Despite much effort CPM has not been scalable to large graphs for medium values of k. Recent work has shown that it is possible to efficiently list all k-cliques in very large real-world graphs for medium values of k. We build on top of this work and scale up CPM. In cases where this first algorithm faces memory limitations, we propose another algorithm, CPMZ, that provides a solution close to the exact one, using more time but less memory.
Finding Heaviest K-Subgraphs And Events In Social Media
2016
Zenodo
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In recent years, social media have become a useful tool to stay in contact with friends, to share thoughts but also to be informed about events. Users can follow news channels, but they can be the ones reporting updates, which distinguishes social media from traditional media. In this paper, we use a graph mining approach for finding events in a graph constructed starting from posts of users. We develop an exact algorithm for solving the heaviest k-subgraph problem which is an NP-hard problem.
doi:10.5281/zenodo.159769
fatcat:3nsuinb4rjhqnefqrygvjcg7tm
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... ur experimental analysis on large real-world graphs shows that our algorithm is able to compute the exact solutions for k up to 15 or more depending on the structure of the graph. We also develop an approximation version of our algorithm scaling to larger k. In comparison, for this setting, the classical heuristic based on weighted core decomposition only leads to sub-optimal solutions. Finally, we show that our algorithm can be used to find relevant events in Twitter. Indeed, as an event is usually described by a small number of words, our algorithm is a useful tool to detect them. Our C code is publicly available: https://github.com/maxdan94/HkS.
Multi-ego-centered communities in practice
2014
Social Network Analysis and Mining
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We propose here a framework to unfold the ego-centered community structure of a given node in a network. The framework is not based on the optimization of a quality function, but on the study of the irregularity of the decrease of a proximity measure. It is a practical use of the notion of multi-ego-centered community and we validate the pertinence of the approach on benchmarks and a real-world network of wikipedia pages. Keywords ego-centered community · multi-ego-centered community ·
doi:10.1007/s13278-014-0180-x
fatcat:y3erw36pgfeb3cpxhwytd5zyjy
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... measure 1 Context and related work Many real-world complex systems, such as social networks or computer networks can be modeled as large graphs, called complex networks. Because of the increasing volume of data available and the need to understand such huge systems, complex networks have been extensively studied these last ten years. Due to its applications, notably in market research and classification, and its intriguing nature, the notion of communities of nodes 1 and their detection has been at the center of this research. For an extensive survey on community detection, we refer to the 2010 review by Fortunato [FOR10] . 1 The idea that there are groups of nodes which are very connected to one-another, but loosely connected to the outside.
Unfolding Ego-Centered Community Structures with "A Similarity Approach"
[chapter]
2013
Studies in Computational Intelligence
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We propose a framework to unfold the ego-centered community structure of a given node in a network. The framework is not based on the optimization of a quality function, but on the study of the irregularity of the decrease of a similarity measure. It is a practical use of the notion of multi-ego-centered community and we validate the pertinence of the approach on a real-world network of wikipedia pages.
doi:10.1007/978-3-642-36844-8_14
fatcat:kvprcfdtgvafxkm4img62eh66q
Towards multi-ego-centred communities: a node similarity approach
2013
International Journal of Web Based Communities
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The community structure of a graph is dened in various ways in the literature: (i) Partition, where nodes can belong to only one community. This vision is unrealistic and may lead to poor results because most nodes belong to several communities in real-world networks. (ii) Overlapping community structure, which is the most natural view, but is often very dicult to identify in practice due to the complex structure of real-world networks, and the huge number of such possible communities. (iii)
doi:10.1504/ijwbc.2013.054906
fatcat:ttcbdszz2vg63bnfdbqdc3azsm
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... -centered community which focuses on individual nodes' communities and seems to be a good compromise. In this paper we investigate the third vision; we propose a new similarity measure between nodes based on opinion dynamics to unfold ego-centered communities. We call it the carryover opinion. In addition to be parameter-free, the carryover opinion can be calculated in a very time-ecient way and can thus be used in very large graphs. We also go further in the idea of ego-centered communities by introducing the new concept of multi-ego-centered communities, i.e., focusing on the communities of a set of nodes rather than of a single node. A key idea is that, although one node generally belongs to numerous communities, a small set of appropriate nodes can fully characterize a single community.
Mining bipartite graphs to improve semantic pedophile activity detection
2014
2014 IEEE Eighth International Conference on Research Challenges in Information Science (RCIS)
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Peer-to-peer (P2P) networks are popular to exchange large volumes of data through the Internet. Pedophile activity is a very important topic for our society and some works have recently attempted to gauge the extent of pedophile exchanges on P2P networks. A key issue is to obtain an efficient detection tool, which may decide if a sequence of keywords is related to the topic or not. We propose to use social network analysis in a large dataset from a P2P network to improve a state-of-the-art
doi:10.1109/rcis.2014.6861035
dblp:conf/rcis/FournierD14
fatcat:xb5veusj5zaaxoomk7vjkxldzm
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... r for pedophile queries. We obtain queries and thus combinations of words which are not tagged by the filter but should be. We also perform some experiments to explore if the original four categories of paedophile queries were to be found by topological measures only.
Listing k-cliques in Sparse Real-World Graphs*
2018
Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18
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Motivated by recent studies in the data mining community which require to efficiently list all k-cliques, we revisit the iconic algorithm of Chiba and Nishizeki and develop the most efficient parallel algorithm for such a problem. Our theoretical analysis provides the best asymptotic upper bound on the running time of our algorithm for the case when the input graph is sparse. Our experimental evaluation on large real-world graphs shows that our parallel algorithm is faster than state-of-the-art
doi:10.1145/3178876.3186125
dblp:conf/www/DanischBS18
fatcat:3jvzxofdrfegte4zuyh3avlkfy
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... algorithms, while boasting an excellent degree of parallelism. In particular, we are able to list all k-cliques (for any k) in graphs containing up to tens of millions of edges as well as all 10-cliques in graphs containing billions of edges, within a few minutes and a few hours respectively. Finally, we show how our algorithm can be employed as an effective subroutine for finding the k-clique core decomposition and an approximate k-clique densest subgraphs in very large real-world graphs.
Model of random packings of different size balls
2010
Physical Review E
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We develop a model to describe the properties of random assemblies of polydisperse hard spheres. We show that the key features to describe the system are (i) the dependence between the free volume of a sphere and the various coordination numbers between the species, and (ii) the dependence of the coordination numbers with the concentration of species; quantities that are calculated analytically. The model predicts the density of random close packing and random loose packing of polydisperse
doi:10.1103/physreve.81.051303
pmid:20866222
fatcat:ixmewhwxjvb5pduzi5jk2xvgym
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... ms for a given distribution of ball size and describes packings for any interparticle friction coefficient. The formalism allows to determine the optimal packing over different distributions and may help to treat packing problems of non-spherical particles which are notoriously difficult to solve.
Large Scale Density-friendly Graph Decomposition via Convex Programming
2017
Proceedings of the 26th International Conference on World Wide Web - WWW '17
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Algorithms for finding dense regions in an input graph have proved to be effective tools in graph mining and data analysis. Recently, Tatti and Gionis [WWW 2015] presented a novel graph decomposition (known as the locally-dense decomposition) that is similar to the well-known k-core decomposition, with the additional property that its components are arranged in order of their densities. Such a decomposition provides a valuable tool in graph mining. Unfortunately, their algorithm for computing
doi:10.1145/3038912.3052619
dblp:conf/www/DanischCS17
fatcat:wnbxddby3nbkfarre5bxrujw3e
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... e exact decomposition is based on a maximum-flow algorithm which cannot scale to massive graphs, while the approximate decomposition defined by the same authors misses several interesting properties. This calls for scalable algorithms for computing such a decomposition. In our work, we devise an efficient algorithm which is able to compute exact locally-dense decompositions in real-world graphs containing up to billions of edges. Moreover, we provide a new definition of approximate locally-dense decomposition which retains most of the properties of an exact decomposition, for which we devise an algorithm that can scale to real-world graphs containing up to tens of billions of edges. Our algorithm is based on the classic Frank-Wolfe algorithm which is similar to gradient descent and can be efficiently implemented in most of the modern architectures dealing with massive graphs. We provide a rigorous study of our algorithms and their convergence rates. We conduct an extensive experimental evaluation on multi-core architectures showing that our algorithms con- † . verge much faster in practice than their worst-case analysis. Our algorithm is even more efficient for the more specialized problem of computing a densest subgraph.
Direct generation of random graphs exactly realising a prescribed degree sequence
2014
2014 6th International Conference on Computational Aspects of Social Networks
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This paper intends to extend the possibilites available to researchers for the evaluation of directed networks with the use of randomly generated graphs. The direct generation of a simple network with a prescribed degree sequence still seems to be an open issue, since the prominent configuration model usually does not realise the degree distribution exactly. We propose such an algorithm using a heuristic for node prioritisation. We demonstrate that the algorithm samples approximately uniformly.
doi:10.1109/cason.2014.6920418
dblp:conf/cason/ObradovicD14
fatcat:7rlprltgpvhx7ov7kvw2pw5ycm
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... In comparison to the switching Markov Chain Monte Carlo algorithms, the direct generation of edges allows an easy modification of the linking behaviour in the random graph, introducing for example degree correlations, mixing patterns or community structure. That way, more specific random graphs can be generated (non-uniformly) in order to test hypotheses on the question, whether specific network features are due to a specific linking behaviour only. Or it can be used to generate series of synthetic benchmark networks with a specific community structure, including hierarchies and overlaps.
Learning a proximity measure to complete a community
2014
2014 International Conference on Data Science and Advanced Analytics (DSAA)
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In large-scale online complex networks (Wikipedia, Facebook, Twitter, etc.) finding nodes related to a specific topic is a strategic research subject. This article focuses on two central notions in this context: communities (groups of highly connected nodes) and proximity measures (indicating whether nodes are topologically close). We propose a parameterized proximity measure which, given a set of nodes belonging to a community, learns the optimal parameters and identifies the other nodes of
doi:10.1109/dsaa.2014.7058057
dblp:conf/dsaa/DanischGG14
fatcat:px6es7cazjboxjqirl2apkos7i
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... s community, called multi-ego-centered community as it is centered on a set of nodes. We validate our results on a large dataset of categorized Wikipedia pages and on benchmarks, we also show that our approach performs better than existing ones. Our main contributions are (i) a new ergonomic parametrized proximity measure, (ii) the automatic tuning of the proximity's parameters and (iii) the unsupervised detection of community boundaries.
A reliable and evolutive web application to detect social capitalists
2015
Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015 - ASONAM '15
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We first introduce a method to detect accurately social capitalists presented by Danisch et al. [1] . ...
Danisch et al. [1] obtained an Accuracy of 86% (correctly labelled examples) with this algorithm. ...
doi:10.1145/2808797.2808799
dblp:conf/asunam/DuguePDBDKMD15
fatcat:s7mchqxqjbc37hgufqlewoamby
Calculation of the Voronoi boundary for lens-shaped particles and spherocylinders
2013
Journal of Statistical Mechanics: Theory and Experiment
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2013 pre-print arXiv:1102.0608v2 fatcat:kvqm5kvvs5adfif7yff4ch5ykiOther Versions
We have recently developed a mean-field theory to estimate the packing fraction of non-spherical particles [A. Baule et al., Nature Commun. (2013)]. The central quantity in this framework is the Voronoi excluded volume, which generalizes the standard hard-core excluded volume appearing in Onsager's theory. The Voronoi excluded volume is defined from an exclusion condition for the Voronoi boundary between two particles, which is usually not tractable analytically. Here, we show how the technical
doi:10.1088/1742-5468/2013/11/p11009
fatcat:ffjf67a725h6bkxgfjdgf7gp3e
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... difficulties in calculating the Voronoi boundary can be overcome for lens-shaped particles and spherocylinders, two standard prolate and oblate shapes with rotational symmetry. By decomposing these shapes into unions and intersections of spheres analytical expressions can be obtained.
Social system as complex networks
2014
Social Network Analysis and Mining
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In the article ''Multiego-centered communities in practice'' by Maximilien Danisch, Jean-Loup Guillaume and Benedicte Le Grand a framework is provided to uncover the ego-centered community structure of ...
doi:10.1007/s13278-014-0238-9
fatcat:dytjtobyvjhrzkpt2xmtcgtjoq
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