12 research outputs found
Towards realistic artificial benchmark for community detection algorithms evaluation
Assessing the partitioning performance of community detection algorithms is
one of the most important issues in complex network analysis. Artificially
generated networks are often used as benchmarks for this purpose. However,
previous studies showed their level of realism have a significant effect on the
algorithms performance. In this study, we adopt a thorough experimental
approach to tackle this problem and investigate this effect. To assess the
level of realism, we use consensual network topological properties. Based on
the LFR method, the most realistic generative method to date, we propose two
alternative random models to replace the Configuration Model originally used in
this algorithm, in order to increase its realism. Experimental results show
both modifications allow generating collections of community-structured
artificial networks whose topological properties are closer to those
encountered in real-world networks. Moreover, the results obtained with eleven
popular community identification algorithms on these benchmarks show their
performance decrease on more realistic networks
Exploring the Evolution of Node Neighborhoods in Dynamic Networks
Dynamic Networks are a popular way of modeling and studying the behavior of
evolving systems. However, their analysis constitutes a relatively recent
subfield of Network Science, and the number of available tools is consequently
much smaller than for static networks. In this work, we propose a method
specifically designed to take advantage of the longitudinal nature of dynamic
networks. It characterizes each individual node by studying the evolution of
its direct neighborhood, based on the assumption that the way this neighborhood
changes reflects the role and position of the node in the whole network. For
this purpose, we define the concept of \textit{neighborhood event}, which
corresponds to the various transformations such groups of nodes can undergo,
and describe an algorithm for detecting such events. We demonstrate the
interest of our method on three real-world networks: DBLP, LastFM and Enron. We
apply frequent pattern mining to extract meaningful information from temporal
sequences of neighborhood events. This results in the identification of
behavioral trends emerging in the whole network, as well as the individual
characterization of specific nodes. We also perform a cluster analysis, which
reveals that, in all three networks, one can distinguish two types of nodes
exhibiting different behaviors: a very small group of active nodes, whose
neighborhood undergo diverse and frequent events, and a very large group of
stable nodes
A Method for Characterizing Communities in Dynamic Attributed Complex Networks
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 try to 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.Comment: IEEE/ACM International Conference on Advances in Social Network
Analysis and Mining (ASONAM), P\'ekin : China (2014
A Comparison of Community Detection Algorithms on Artificial Networks
International audienceCommunity 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, most 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
Author manuscript, published in "1st International Conference on Networked Digital Technologies, Ostrava: Czech Republic (2009)" Relative Evaluation of Partition Algorithms for Complex Networks
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 by Lancichinetti et al. to compare seven algorithms
Ganetto - Galatasaray Network Toolbox
These R scripts are designed to handle various tasks related to the community structure of complex networks: community detection, generation of modular artificial graphs, evaluation of communinity detection results, assessment of various topological measures, generation of plots
Last FM, Jazz Listeners, Dynamic Network
<div>LastFM is a music website that allows its members to register and listen to music online. It is also a social network platform, because the members can declare friendship relationships. In LastFM, members can join a predefined groups related to their music tastes, and participate in music-related events such as concerts. Using the LastFM API, One can retrieve the information of the artist and track a user has listened to, with the exact timestamp. Moreover, it is also possible to get some information regarding the music-related events the users joined, including the exact timestamps.</div><div><br></div><div>We extracted a network by focusing on the members of the group Jazz, which is supposed to include users appreciating this type of music. We took advantage of the LastFM API to retrieve the members of this group and the existing friendship connection between them. In the end, our network contains 1702 nodes representing the Jazz users. The friendship relationships between them is static, though, in</div><div>the sense the LastFM API does not give access to any temporal information regarding their beginning or end. So, we decided to take advantage of some additional information to get a dynamic structure. We put a link between two nodes if two conditions were simultaneously true: 1) both considered users listened to at least one common artist for a specific period of time, and 2) they are friends on the LastFM platform. For the mentioned period of time, we decided to use 3 months with 1 month overlap, after having analyzed the dynamics of the platform. In other words, we extracted a dynamic network in which each time slice represents three months of LastFM usage for our 1702 users of interest. There are one month overlap between two consecutive time slices.</div
Une méthode pour caractériser les communautés des réseaux dynamiques à attributs
National audienceDe nombreux systèmes complexes sont étudiés via l'analyse de réseaux dits complexes ayant des propriétés topologiques typiques. Parmi cellesci, les structures de communautés sont particulièrement étudiées. De nombreuses méthodes permettent de les détecter, y compris dans des réseaux contenant des attributs nodaux, des liens orientés ou évoluant dans le temps. La détection prend la forme d'une partition de l'ensemble des noeuds, qu'il faut ensuite caractériser relativement au système modélisé. Nous travaillons sur l'assistance à cette tâche de caractérisation. Nous proposons une représentation des réseaux sous la forme de séquences de descripteurs de noeuds, qui combinent les informations temporelles, les mesures topologiques, et les valeurs des attributs nodaux. Les communautés sont caractérisées au moyen des motifs séquentiels émergents les plus représentatifs issus de leurs noeuds. Ceci permet notamment la détection de comportements inhabituels au sein d'une communauté. Nous décrivons une étude empirique sur un réseau de collaboration scientifique. -------------- 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 a characterization 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
Finding proper time intervals for dynamic network extraction
Extracting a proper dynamic network for modeling 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 use statistically 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 modeling
Interpreting communities based on the evolution of a dynamic attributed network
International audienceMany 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 of data 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