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Many complex systems are modeled through complex networks whose analysis reveals typical topological properties. Amongst those, the community structure is one of the most studied. Many methods are proposed to detect communities, not only in plain, but also in attributed, directed or even dynamic networks. A community structure takes the form of a partition of the node set, which must then be characterized relatively to the properties of the studied system. We propose a method to support such aarXiv:1312.4676v1 fatcat:qyalaox4bzhnvesrn2pt42smue
more »... haracterization task. We define a sequence-based representation of networks, combining temporal information, topological measures, and nodal attributes. We then characterize communities using the most representative emerging sequential patterns of its nodes. This also allows detecting unusual behavior in a community. We describe an empirical study of a network of scientific collaborations.---De nombreux syst\'emes complexes sont \'etudi\'es via l'analyse de r\'eseaux dits complexes ayant des propri\'et\'es topologiques typiques. Parmi cellesci, les structures de communaut\'es sont particuli\'erement \'etudi\'ees. De nombreuses m\'ethodes permettent de les d\'etecter, y compris dans des r\'eseaux contenant des attributs nodaux, des liens orient\'es ou \'evoluant dans le temps. La d\'etection prend la forme d'une partition de l'ensemble des noeuds, qu'il faut ensuite caract\'eriser relativement au syst\'eme mod\'elis\'e. Nous travaillons sur l'assistance \'a cette t\^ache de caract\'erisation. Nous proposons une repr\'esentation des r\'eseaux sous la forme de s\'equences de descripteurs de noeuds, qui combinent les informations temporelles, les mesures topologiques, et les valeurs des attributs nodaux. Les communaut\'es sont caract\'eris\'ees au moyen des motifs s\'equentiels \'emergents les plus repr\'esentatifs issus de leurs noeuds. Ceci permet notamment la d\'etection de comportements inhabituels au sein d'une communaut\'e. Nous d\'ecrivons une \'etude empirique sur un r\'eseau de collaboration scientifique.
Lecture Notes in Computer Science
Community detection has become a very important part in complex networks analysis. Authors traditionally test their algorithms on a few real or artificial networks. Testing on real networks is necessary, but also limited: the considered real networks are usually small, the actual underlying communities are generally not defined objectively, and it is not possible to control their properties. Generating artificial networks makes it possible to overcome these limitations. Until recently though,doi:10.1007/978-3-642-04747-3_20 fatcat:soev7rp3ljdbvekagg3uvj47ay
more »... st works used variations of the classic Erdős-Rényi random model and consequently suffered from the same flaws, generating networks not realistic enough. In this work, we use Lancichinetti et al. model, which is able to generate networks with controlled power-law degree and community distributions, to test some community detection algorithms. We analyze the properties of the generated networks and use the normalized mutual information measure to assess the quality of the results and compare the considered algorithms.
2009 First International Conference on Networked Digital Technologies
Complex networks partitioning consists in identifying denser groups of nodes. This popular research topic has applications in many fields such as biology, social sciences and physics. This led to many different partition algorithms, most of them based on Newman's modularity measure, which estimates the quality of a partition. Until now, these algorithms were tested only on a few real networks or unrealistic artificial ones. In this work, we use the more realistic generative model developed bydoi:10.1109/ndt.2009.5272078 fatcat:ldmvxk544feajf77wqfzt3v2du
more »... ncichinetti et al. to compare seven algorithms: Edge-betweenness, Eigenvector, Fast Greedy, Label Propagation, Markov Clustering, Spinglass and Walktrap. We used normalized mutual information (NMI) to assess their performances. Our results show Spinglass and Walktrap are above the others in terms of quality, while Markov Clustering and Edge-Betweenness also achieve good performance. Additionally, we compared NMI and modularity and observed they are not necessarily related: some algorithms produce better partitions while getting lower modularity.
Communications in Computer and Information Science
Community detection is a very active field in complex networks analysis, consisting in identifying groups of nodes more densely interconnected relatively to the rest of the network. The existing algorithms are usually tested and compared on real-world and artificial networks, their performance being assessed through some partition similarity measure. However, artificial networks realism can be questioned, and the appropriateness of those measures is not obvious. In this study, we take advantagedoi:10.1007/978-3-642-22027-2_23 fatcat:oznqoolndnbsbdb6ixq2y3sofi
more »... of recent advances concerning the characterization of community structures to tackle these questions. We first generate networks thanks to the most realistic model available to date. Their analysis reveals they display only some of the properties observed in real-world community structures. We then apply five community detection algorithms on these networks and find out the performance assessed quantitatively does not necessarily agree with a qualitative analysis of the identified communities. It therefore seems both approaches should be applied to perform a relevant comparison of the algorithms.
Oxygenation conditions are crucial for growth and tumor progression. Recent data suggests a decrease in cancer cell proliferation occurring after exposure to normobaric hyperoxia. Those changes are associated with fractal dimension. The purpose of this research was to study the impact of hyperoxia on apoptosis and morphology of leukemia cell lines. Two hematopoietic lymphoid cancer cell lines (a T-lymphoblastoid line, JURKAT and a B lymphoid line, CCRF-SB) were tested under conditions ofdoi:10.3390/biom10020282 pmid:32059539 pmcid:PMC7072400 fatcat:ykdatvm46vg3vaqnhuwlxlxhza
more »... ric hyperoxia (FiO2 > 60%, ± 18h) and compared to a standard group (FiO2 = 21%). We tested for apoptosis using a caspase-3 assay. Cell morphology was evaluated by cytospin, microphotography after coloration, and analysis by a fractal dimension calculation software. Our results showed that exposure of cell cultures to transient normobaric hyperoxia induced apoptosis (elevated caspase-3) as well as significant and precocious modifications in cell complexity, as highlighted by increased fractal dimensions in both cell lines. These features are associated with changes in structure (pycnotic nucleus and apoptosis) recorded by microscopic analysis. Such morphological alterations could be due to several molecular mechanisms and rearrangements in the cancer cell, leading to cell cycle inhibition and apoptosis as shown by caspase-3 activity. T cells seem less resistant to hyperoxia than B cells.
Community detection is one of the most active fields in complex networks analysis, due to its potential value in practical applications. Many works inspired by different paradigms are devoted to the development of algorithmic solutions allowing to reveal the network structure in such cohesive subgroups. Comparative studies reported in the literature usually rely on a performance measure considering the community structure as a partition (Rand Index, Normalized Mutual information, etc.).doi:10.1088/1742-5468/2012/08/p08001 fatcat:fjbq3jrjkba67cxtvwdpz7g2cq
more »... this type of comparison neglects the topological properties of the communities. In this article, we present a comprehensive comparative study of a representative set of community detection methods, in which we adopt both types of evaluation. Community-oriented topological measures are used to qualify the communities and evaluate their deviation from the reference structure. In order to mimic real-world systems, we use artificially generated realistic networks. It turns out there is no equivalence between both approaches: a high performance does not necessarily correspond to correct topological properties, and vice-versa. They can therefore be considered as complementary, and we recommend applying both of them in order to perform a complete and accurate assessment.
Extracting a proper dynamic network for modelling a time-dependent complex system is an important issue. Building a correct model is related to finding out critical time points where a system exhibits considerable change. In this work, we propose to measure network similarity to detect proper time intervals. We develop three similarity metrics, node, link, and neighborhood similarities, for any consecutive snapshots of a dynamic network. Rather than a label or a user-defined threshold, we usedoi:10.1088/1742-5468/abed45 fatcat:z4gpouujwjft7otgeafekraq3y
more »... atistically expected values of proposed similarities under a null-model to state whether the system changes critically. We experimented on two different data sets with different temporal dynamics: The Wi-Fi access points logs of a university campus and Enron emails. Results show that, first, proposed similarities reflect similar signal trends with network topological properties with less noisy signals, and their scores are scale invariant. Second, proposed similarities generate better signals than adjacency correlation with optimal noise and diversity. Third, using statistically expected values allows us to find different time intervals for a system, leading to the extraction of non-redundant snapshots for dynamic network modelling.
Preliminary results have been presented in Orman and Labatut (2010) . ... ., 2005; Orman et al., 2011a) . Indeed, generative models allow producing easily and quickly large collections of such networks. ...doi:10.1504/ijwbc.2013.054908 fatcat:hobmpgoajvgn5ouxav65p24pde
Many methods have been proposed to detect communities, not only in plain, but also in attributed, directed or even dynamic complex networks. From the modeling point of view, to be of some utility, the community structure must be characterized relatively to the properties of the studied system. However, most of the existing works focus on the detection of communities, and only very few try to tackle this interpretation problem. Moreover, the existing approaches are limited either by the type ofdoi:10.1007/s13278-015-0262-4 fatcat:bqmjevr6rncxbezxah4s2b6o6a
more »... ata they handle, or by the nature of the results they output. In this work, we see the interpretation of communities as a problem independent from the detection process, consisting in identifying the most characteristic features of communities. We give a formal definition of this problem and propose a method to solve it. To this aim, we first define a sequence-based representation of networks, combining temporal information, community structure, topological measures, and nodal attributes. We then describe how to identify the most emerging sequential patterns of this dataset, and use them to characterize the communities. We study the performance of our method on artificially generated dynamic attributed networks. We also empirically validate our framework on real-world systems: a DBLP network of scientific collaborations, and a LastFM network of social and musical interactions.
LastFM Network The LastFM dynamic network considered in this work was first used in (Orman et al. 2015) . ... Orman et al. (2014) use sequential pattern mining to make sense of dynamic communities and ease their interpretation. ...doi:10.1016/j.physa.2017.04.084 fatcat:vhvop5q5xvhkjmqrq3z7ncbtau
Many methods have been proposed to detect communities, not only in plain, but also in attributed, directed or even dynamic complex networks. In its simplest form, a community structure takes the form of a partition of the node set. From the modeling point of view, to be of some utility, this partition must then be characterized relatively to the properties of the studied system. However, if most of the existing works focus on defining methods for the detection of communities, only very few trydoi:10.1109/asonam.2014.6921629 dblp:conf/asunam/OrmanLPB14 fatcat:ktfu3p4cyjacvhgepdc2hngfbi
more »... o tackle this interpretation problem. Moreover, the existing approaches are limited either in the type of data they handle, or by the nature of the results they output. In this work, we propose a method to efficiently support such a characterization task. We first define a sequence-based representation of networks, combining temporal information, topological measures, and nodal attributes. We then describe how to identify the most emerging sequential patterns of this dataset, and use them to characterize the communities. We also show how to detect unusual behavior in a community, and highlight outliers. Finally, as an illustration, we apply our method to a network of scientific collaborations.
IEEE EUROCON 2021 - 19th International Conference on Smart Technologies
Keziban Orman and Serhat Çolak 227 94 Scalabeling: Linear Slider Supported Labeling for the Classification of Streaming Data Christine Steinmeier, Jan Budke and Dominic Becking 233 _________ ... Infrared Image Registration for Face Detection Palani Thanaraj Krishnan, Parvathavarthini Balasubramanian and Vijay Jeyakumar 222 91 Similarity Based Compression Ratio for Dynamic Network Modelling Günce ...doi:10.1109/eurocon52738.2021.9535646 fatcat:l457sd2wfzevxgbn6hvlqnwyca
Gunce Keziban Orman and Vincent Labatut (2011), have discussed the properties of complex network and compare the five community detection algorithms by using a set of artificial networks Heather Kreger ...doi:10.14257/ijhit.2015.8.7.07 fatcat:phdnpqbayvh3beapkxshrpfqwm