A comparative study of overlapping community detection methods from the perspective of the structural properties

Vieira, Vinícius da Fonseca; Xavier, Carolina Ribeiro; Evsukoff, Alexandre G.

doi:10.1007/s41109-020-00289-9

Cited by 37 publications

(26 citation statements)

References 43 publications

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“…in our case, a district belongs to one and only one community 67 . Yet in real-world networks, communities have important overlapping and nested communities 92,93 . Detecting overlapping districts may unveil "bridges" between pig trade communities which may be crucial for both epizootic and endemic disease control efforts.…”

Section: Discussionmentioning

confidence: 99%

Network analysis of pig movement data as an epidemiological tool: an Austrian case study

Puspitarani

Fuchs

et al. 2023

Preprint

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Animal movements represent a major risk for the spread of infectious diseases in the domestic swine population. In this study, we adopted methods from social network analysis to explore pig trades in Austria. We used a dataset of daily records of swine movements covering the period 2015-2021. We analyzed the topology of the network and its structural changes over time, including seasonal and long-term variations in the pig production activities. Finally, we studied the temporal dynamics of the network community structure. Our findings show that the Austrian pig production was dominated by small-sized farms while spatial farm density was heterogeneous. The network exhibited a scale-free topology but was very sparse, suggesting a moderate impact of infectious disease outbreaks. However, two regions (Upper Austria and Styria) may present a higher structural vulnerability. The network also showed very high assortativity between holdings from the same federal state. Dynamic community detection revealed a stable behavior of the clusters. Yet trade communities did not correspond to sub-national administrative divisions and may be an alternative zoning approach to managing infectious diseases. Knowledge about the topology, contact patterns, and temporal dynamics of the pig trade network can support optimized risk-based disease control and surveillance strategies.

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Section: Discussionmentioning

confidence: 99%

Network analysis of pig movement data as an epidemiological tool: an Austrian case study

Puspitarani

Fuchs

et al. 2023

Preprint

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“…The modularity index, Q, measures the fraction of edges in communities, compared to the expected ratio when all edges are placed randomly [13]. We use the modularity Q as defined in [75][76][77][78][79] for the comparison. We also compare calculated modularity values with the degree of positive assortment in our interaction network in the individual rounds of the simulation model.…”

Section: Results and Discussion On Simulation Experimentsmentioning

confidence: 99%

The Use of a Game Theory Model to Explore the Emergence of Core/Periphery Structure in Networks and Its Symmetry

Beránek

Remeš

2021

Symmetry

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In network systems characterized by complex interactions of various types, core-periphery structures can be found. In this paper, we deal with such questions as what processes can lead to the emergence of core-periphery formation, whether this structure is symmetric, and to what extent. Namely, the question of symmetry in a complex network is still the subject of intense research interest. Symmetry can relate to network topology, network relationships, and other processes on networks. To answer these questions, we modified the model of the classic social dilemma called the repeated prisoner’s dilemma (or repeated PD game) by adding the cost of maintaining relationships between the pairs of players (partners) and especially by adding the possibility of ending some relationships. We present the results of simulations that suggest that the players’ network strategy (i.e., partner selection or termination of relationships with some partners) is the driving force behind the emergence of a core-periphery structure in networks rather than the player’s strategy in PD. Our results also suggest that the formed core is symmetric, and this symmetry is a result of the symmetric interactions of core players. Our outcomes can help understand various economic or social questions related to creating centers or peripheries, including their symmetry in different network systems.

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“…The creation of the partitions maps relies on a preliminary step of detecting overlapping communities in the observed network. The latter is a hard task, especially when utilizing only structural properties [ 63 ]. The combination of the dependency on detecting the overlapping communities, and the fact overlapping between communities is essential for CMMAC , presents a limitation of our approach.…”

Section: Discussionmentioning

confidence: 99%

Co-Membership-based Generic Anomalous Communities Detection

Lapid

Kagan

Fire

2023

Neural Process Lett

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Nowadays, detecting anomalous communities in networks is an essential task in research, as it helps discover insights into community-structured networks. Most of the existing methods leverage either information regarding attributes of vertices or the topological structure of communities. In this study, we introduce the Co-Membership-based Generic Anomalous Communities Detection Algorithm (referred as to CMMAC ), a novel and generic method that utilizes the information of vertices co-membership in multiple communities. CMMAC is domain-free and almost unaffected by communities’ sizes and densities. Specifically, we train a classifier to predict the probability of each vertex in a community being a member of the community. We then rank the communities by the aggregated membership probabilities of each community’s vertices. The lowest-ranked communities are considered to be anomalous. Furthermore, we present an algorithm for generating a community-structured random network enabling the infusion of anomalous communities to facilitate research in the field. We utilized it to generate two datasets, composed of thousands of labeled anomaly-infused networks, and published them. We experimented extensively on thousands of simulated, and real-world networks, infused with artificial anomalies. CMMAC outperformed other existing methods in a range of settings. Additionally, we demonstrated that CMMAC can identify abnormal communities in real-world unlabeled networks in different domains, such as Reddit and Wikipedia.

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A comparative study of overlapping community detection methods from the perspective of the structural properties

Cited by 37 publications

References 43 publications

Network analysis of pig movement data as an epidemiological tool: an Austrian case study

Network analysis of pig movement data as an epidemiological tool: an Austrian case study

The Use of a Game Theory Model to Explore the Emergence of Core/Periphery Structure in Networks and Its Symmetry

Co-Membership-based Generic Anomalous Communities Detection

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