Generating Attributed Networks with Communities

Largeron, Christine; Mougel, Pierre-Nicolas; Rabbany, Reihaneh; Zaı̈ane, Osmar R.

doi:10.1371/journal.pone.0122777

Cited by 47 publications

(20 citation statements)

References 44 publications

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“…A model for generating attributed networks with communities is presented in [67]. This model is described as "similar to the BTER model" (described in Section 5.2.5), except that it uses the similarity of the vertex attributes to determine the inter-cluster edges, while BTER uses a scale-free distribution.…”

Section: Attributed Network With Communitiesmentioning

confidence: 99%

Node-Based Resilience Measure Clustering with Applications to Noisy and Overlapping Communities in Complex Networks

et al. 2018

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This paper examines a schema for graph-theoretic clustering using node-based resilience measures. Node-based resilience measures optimize an objective based on a critical set of nodes whose removal causes some severity of disconnection in the network. Beyond presenting a general framework for the usage of node based resilience measures for variations of clustering problems, we experimentally validate the usefulness of such methods in accomplishing the following: (i) clustering a graph in one step without knowing the number of clusters a priori; (ii) removing noise from noisy data; and (iii) detecting overlapping communities. We demonstrate that this clustering schema can be applied successfully using a wide range of data, including both real and synthetic networks, both natively in graph form and also expressed as point sets.

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Section: Attributed Network With Communitiesmentioning

confidence: 99%

Node-Based Resilience Measure Clustering with Applications to Noisy and Overlapping Communities in Complex Networks

et al. 2018

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“…Simulated networks are employed in the community-detection experiment to overcome the difficulty to evaluate communities in real-world networks due to an absence of community ground-truths (Cao et al 2015), and to assess communityquality under varying degrees of structural parameters. Nevertheless, LFR Benchmark networks do simulate networks that are very close to real-world social networks' data (Bródka et al 2010), and this benchmark is becoming a de-facto standard network-generator for evaluating the performance of different community-detection algorithms (Largeron et al 2015). We generated a network of 10,000 nodes using LFR benchmark for the first experiment.…”

Section: Datasetsmentioning

confidence: 99%

A fuzzy logic approach to influence maximization in social networks

Atif

AlFalahi

Wangchuk

et al. 2019

J Ambient Intell Human Comput

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Within a community, social relationships are paramount to profile individuals' conduct. For instance, an individual within a social network might be compelled to embrace a behaviour that his/her companion has recently adopted. Such social attitude is labelled social influence, which assesses the extent by which an individual's social neighbourhood adopt that individual's behaviour. We suggest an original approach to influence maximization using a fuzzy-logic based model, which combines influence-weights associated with historical logs of the social network users, and their favourable location in the network. Our approach uses a two-phases process to maximise influence diffusion. First, we harness the complexity of the problem by partitioning the network into significantly-enriched community-structures, which we then use as modules to locate the most influential nodes across the entire network. These key users are determined relatively to a fuzzy-logic based technique that identifies the most influential users, out of which the seed-set candidates to diffuse a behaviour or an innovation are extracted following the allocated budget for the influence campaign. This way to deal with influence propagation in social networks, is different from previous models, which do not compare structural and behavioural attributes among members of the network. The performance results show the validity of the proposed partitioning-approach of a social network into communities, and its contribution to "activate" a higher number of nodes overall. Our experimental study involves both empirical and real contemporary social-networks, whereby a smaller seed set of key users, is shown to scale influence to the high-end compared to some renowned techniques, which employ a larger seed set of key users and yet they influence less nodes in the social network.Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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“…We used the generator DANCer [29,5] to construct synthetic datasets. A network is defined by a sequence of undirected attributed graphs having a well defined partition of the vertices into non-overlapping communities at each snapshot.…”

Section: Datasetmentioning

confidence: 99%

Community detection in dynamic graphs with missing edges

Benyahia¹,

Largeron²,

Jeudy³

2017

2017 11th International Conference on Research Challenges in Information Science (RCIS)

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Social networks are usually analyzed and mined without taking into account the presence of missing values. In this article, we consider dynamic networks represented by sequences of graphs that change over time and we study the robustness and the accuracy of the community detection algorithms in presence of missing edges. We assume that the network evolution can provide a complementary information allowing to neutralize the missing data. To confirm our hypothesis, we designed an experimental framework to simulate the missing data and compare the communities identified by the methods, with or without missing links. We explore two types of methods. The first ones, based on tensor decomposition, are adapted for dynamic networks. The second ones correspond to conventional community detection algorithms able to handle simple graphs. In our framework, the latter ones are adapted to dynamic graphs, either by merging the data during the preprocessing step or by merging the partitions during a post-processing step. The experimentation was conducted on synthetic and real dynamic networks for which the ground truth is available. The results confirm the best performances of the methods suited for dynamic networks when they present a complex community structure.

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Generating Attributed Networks with Communities

Cited by 47 publications

References 44 publications

Node-Based Resilience Measure Clustering with Applications to Noisy and Overlapping Communities in Complex Networks

Node-Based Resilience Measure Clustering with Applications to Noisy and Overlapping Communities in Complex Networks

A fuzzy logic approach to influence maximization in social networks

Community detection in dynamic graphs with missing edges

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