Gemsec

Rózemberczki, Benedek; Davies, Ryan; Sarkar, Rik; Sutton, Charles

doi:10.1145/3341161.3342890

Cited by 185 publications

(30 citation statements)

References 22 publications

(25 reference statements)

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“…Large real networks. We also tested our algorithm on a number of large real networks across a wide range of magnitude, including the DBLP network and Amazon product network [51], the Enron email network [52], the Facebook user network [53], the five Arxiv collaboration networks [54], the recent data from two digital platforms (Deezer, 3 networks; Facebook, 8 networks) [55], and the Gowalla network and the Brightkite network, both location-based social networks [56]. Detection results are summarized in table 1, and a few critical metrics are visualized in figure 5 and 6.…”

Section: Resultsmentioning

confidence: 99%

Self-falsifiable hierarchical detection of overlapping communities on social networks

Zhang

2020

New J. Phys.

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No community detection algorithm can be optimal for all possible networks, thus it is important to identify whether the algorithm is suitable for a given network. We propose a multi-step algorithmic solution scheme for overlapping community detection based on an advanced label propagation process, which imitates the community formation process on social networks. Our algorithm is parameter-free and is able to reveal the hierarchical order of communities in the graph. The unique property of our solution scheme is self-falsifiability; an automatic quality check of the results is conducted after the detection, and the fitness of the algorithm for the specific network is reported. Extensive experiments show that our algorithm is self-consistent, reliable on networks of a wide range of size and different sorts, and is more robust than existing algorithms on both sparse and large-scale social networks. Results further suggest that our solution scheme may uncover features of networks' intrinsic community structures.Community detection is a central topic in network science. Pioneered by works represented by Palla et al [1], in recent years more and more studies focus on the detection of overlapping communities as opposed to exhaustive communities, a division also regarded as soft-partitioning versus hard-partitioning. Besides relying on the optimization of certain metrics, e.g. modularity [2], conductance [3], fitness [4], or aggregative metric based on link prediction algorithms [5] etc, as the extensively-studied traditional approach for detecting exhaustive communities, for overlapping communities, a lot of new tools are designed based on various ideas, including link communities [6, 7], clique percolation [8], seed set expansion [3,[9][10][11][12], label propagation [13][14][15][16], local spectral clustering method [17], and methods based on statistical inference, such as Infomap [18], the stochastic block model (SBM,[19,20]; in particular, methods adopting the belief propagation algorithm [21,22]), and other generative models [23,24].Despite the success of different solution schemes on various application fronts, some weak points of existing community detection algorithms could be pinned down in practice, which we believe might be problematic in certain cases (see appendix A for a detailed discussion). These weaknesses include: (1) many solution schemes are over-parameterized, and in some cases the tuning of parameters depends largely on unwarranted heuristics;(2) many scalable methods based on the seed set expansion process [2-4, 9, 25-28] may lack well-designed seeding strategies [10,11,29] and often rely on ad-hoc strategies; (3) some algorithms that claim to be local, as opposed to methods based on an optimization over the entire graph, in fact still optimize on the community level and thus do not guarantee complete locality; (4) the number of communities in the graph is often predetermined in certain algorithms, which might not be a good treatment, despite its claimed advantage [30] and the possible determina...

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Section: Resultsmentioning

confidence: 99%

Self-falsifiable hierarchical detection of overlapping communities on social networks

Zhang

2020

New J. Phys.

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“…Download the real datasets from the network repository to experiments. According to the data information required for data transmission in opportunistic social networks, and choose pages-government [43], wiki-elec [44], advogato [45], and slashdot [46] four datasets for simulation experiments. e characteristic information of the four experimental datasets is shown in Table 1.…”

Section: Simulation and Analysismentioning

confidence: 99%

Efficient Data Transmission for Community Detection Algorithm Based on Node Similarity in Opportunistic Social Networks

Xiaokaiti

Qian

2021

Complexity

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With the rapid development of 5G era, the number of messages on the network has increased sharply. The traditional opportunistic networks algorithm has some shortcomings in processing data. Most traditional algorithms divide the nodes into communities and then perform data transmission according to the divided communities. However, these algorithms do not consider enough nodes’ characteristics in the communities’ division, and two positively related nodes may divide into different communities. Therefore, how to accurately divide the community is still a challenging issue. We propose an efficient data transmission strategy for community detection (EDCD) algorithm. When dividing communities, we use mobile edge computing to combine network topology attributes with social attributes. When forwarding the message, we select optimal relay node as transmission according to the coefficients of channels. In the simulation experiment, we analyze the efficiency of the algorithm in four different real datasets. The results show that the algorithm has good performance in terms of delivery ratio and routing overhead.

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“…The infection dynamics match those used in equations (3.10)–(3.12) tuned to be just slightly above the percolation threshold, with strains 1, 2 and 3 marked by +, ° and ◻, respectively. The networks used are ( a ) a trust network of 75 879 nodes from the Epinions social network [28], ( b ) a friendship network from the music streaming service Deezer [29] and ( c ) a network of internal email communication within the company Enron [30]. (Online version in colour.)…”

Section: Co-infectionmentioning

confidence: 99%

Heterogeneous node responses to multi-type epidemics on networks

Moore

Rogers

2020

Proc. R. Soc. A.

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Having knowledge of the contact network over which an infection is spreading opens the possibility of making individualized predictions for the likelihood of different nodes to become infected. When multiple infective strains attempt to spread simultaneously we may further ask which strain, or strains, are most likely to infect a particular node. In this article we investigate the heterogeneity in likely outcomes for different nodes in two models of multi-type epidemic spreading processes. For models allowing co-infection we derive message-passing equations whose solution captures how the likelihood of a given node receiving a particular infection depends on both the position of the node in the network and the interaction between the infection types. For models of competing epidemics in which co-infection is impossible, a more complicated analysis leads to the simpler result that node vulnerability factorizes into a contribution from the network topology and a contribution from the infection parameters.

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Gemsec

Cited by 185 publications

References 22 publications

Self-falsifiable hierarchical detection of overlapping communities on social networks

Self-falsifiable hierarchical detection of overlapping communities on social networks

Efficient Data Transmission for Community Detection Algorithm Based on Node Similarity in Opportunistic Social Networks

Heterogeneous node responses to multi-type epidemics on networks

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