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, Danisch and Tabourier 2021 See https://perf.wiki.kernel.org. ... Cache-mr: proportion of data that was not found in cache (L1, 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
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 ofarXiv:2110.01213v1 fatcat:zhcpu5lhhndb5hhojvmggrw7r4
more »... 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.
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
more »... 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.
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
more »... 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.
Studies in Computational Intelligence
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
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
more »... -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.
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-artdoi:10.1109/rcis.2014.6861035 dblp:conf/rcis/FournierD14 fatcat:xb5veusj5zaaxoomk7vjkxldzm
more »... 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.
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-artdoi:10.1145/3178876.3186125 dblp:conf/www/DanischBS18 fatcat:3jvzxofdrfegte4zuyh3avlkfy
more »... 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.
Physical Review E
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 polydispersedoi:10.1103/physreve.81.051303 pmid:20866222 fatcat:ixmewhwxjvb5pduzi5jk2xvgym
more »... 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.
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 computingdoi:10.1145/3038912.3052619 dblp:conf/www/DanischCS17 fatcat:wnbxddby3nbkfarre5bxrujw3e
more »... 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.
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
more »... 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.
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 ofdoi:10.1109/dsaa.2014.7058057 dblp:conf/dsaa/DanischGG14 fatcat:px6es7cazjboxjqirl2apkos7i
more »... 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.
We first introduce a method to detect accurately social capitalists presented by Danisch et al.  . ... Danisch et al.  obtained an Accuracy of 86% (correctly labelled examples) with this algorithm. ...doi:10.1145/2808797.2808799 dblp:conf/asunam/DuguePDBDKMD15 fatcat:s7mchqxqjbc37hgufqlewoamby
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 technicaldoi:10.1088/1742-5468/2013/11/p11009 fatcat:ffjf67a725h6bkxgfjdgf7gp3e
more »... 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.
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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