Hierarchical Overlapping Community Detection for Weighted Networks

Prokop, Petr; Dráždilová, Pavla; Platoš, Jan

doi:10.1007/978-3-031-53499-7_13

Cited by 2 publications

(4 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Traditionally, community detection methods have assumed that nodes belong to disjoint communities, but real-world networks often exhibit overlapping community structures where nodes can participate in multiple groups. Methods and algorithms for detecting overlapping communities have been studied in [18,[20][21][22][45][46][47] to mention a few. An overlapping community detection method in complex networks based on information theory was presented in [20].…”

Section: Related Workmentioning

confidence: 99%

“…The first algorithm that finds simultaneously both overlapping communities and the hierarchical structure in complex networks was introduced in [21]. Later, overlapping and hierarchical community detection for weighted networks was studied, for example, in [22].…”

Section: Related Workmentioning

confidence: 99%

“…Upon examining the results shown in Figure 2, we can observe the existence of two additional groups of nodes that always remain together. The first group comprises nodes {15, 16,19,21,23,24,27,30,22, 34}, highlighted in red, and the second group includes nodes {1, 2, 4, 8, 12, 13, 14, 18, 20, 22}, marked in dark green. These groups of nodes do not break up into distinct communities and are considered core structures of communities.…”

Section: Zachary's Karate Club Social Networkmentioning

confidence: 99%

“…Communities can be made up of one or more building blocks, and a building block can be a self-contained community or not. The definition of a community is quite general, and it also allows for hierarchical and overlapping communities [10,[18][19][20][21][22]. Our approach, which involves searching for local maxima of the quality function, is well suited for identifying complex and overlapping community structures.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Detecting Overlapping Communities Based on Influence-Spreading Matrix and Local Maxima of a Quality Function

Kuikka

2024

Computation

View full text Add to dashboard Cite

Community detection is a widely studied topic in network structure analysis. We propose a community detection method based on the search for the local maxima of an objective function. This objective function reflects the quality of candidate communities in the network structure. The objective function can be constructed from a probability matrix that describes interactions in a network. Different models, such as network structure models and network flow models, can be used to build the probability matrix, and it acts as a link between network models and community detection models. In our influence-spreading model, the probability matrix is called an influence-spreading matrix, which describes the directed influence between all pairs of nodes in the network. By using the local maxima of an objective function, our method can standardise and help in comparing different definitions and approaches of community detection. Our proposed approach can detect overlapping and hierarchical communities and their building blocks within a network. To compare different structures in the network, we define a cohesion measure. The objective function can be expressed as a sum of these cohesion measures. We also discuss the probability of community formation to analyse a different aspect of group behaviour in a network. It is essential to recognise that this concept is separate from the notion of community cohesion, which emphasises the need for varying objective functions in different applications. Furthermore, we demonstrate that normalising objective functions by the size of detected communities can alter their rankings.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Zachary's Karate Club Social Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Detecting Overlapping Communities Based on Influence-Spreading Matrix and Local Maxima of a Quality Function

Kuikka

2024

Computation

View full text Add to dashboard Cite

show abstract

Overlapping community detection in weighted networks via hierarchical clustering

Prokop,

Dráždilová,

Platoš

2024

PLoS ONE

View full text Add to dashboard Cite

In real-world networks, community structures often appear as tightly connected clusters of nodes, with recent studies suggesting a hierarchical organization where larger groups subdivide into smaller ones across different levels. This hierarchical structure is particularly complex in trade networks, where actors typically belong to multiple communities due to diverse business relationships and contracts. To address this complexity, we present a novel algorithm for detecting hierarchical structures of overlapping communities in weighted networks, focusing on the interdependency between internal and external quality metrics for evaluating the detected communities. The proposed Graph Hierarchical Agglomerative Clustering (GHAC) approach utilizes maximal cliques as the basis units for hierarchical clustering. The algorithm measures dissimilarities between clusters using the minimal closed trail distance (CT−distance) and the size of maximal cliques within overlaps, capturing the density and connectivity of nodes. Through extensive experiments on synthetic networks with known ground truth, we demonstrate that the adjusted Silhouette index is the most reliable internal metric for determining the optimal cut in the dendrogram. Experimental results indicate that the GHAC method is competitive with widely used community detection techniques, particularly in networks with highly overlapping communities. The method effectively reveals the hierarchical structure of communities in weighted networks, as demonstrated by its application to the OECD weighted trade network, which describes the balanced trade value of bilateral trade relations.

show abstract

Hierarchical Overlapping Community Detection for Weighted Networks

Cited by 2 publications

References 39 publications

Detecting Overlapping Communities Based on Influence-Spreading Matrix and Local Maxima of a Quality Function

Detecting Overlapping Communities Based on Influence-Spreading Matrix and Local Maxima of a Quality Function

Overlapping community detection in weighted networks via hierarchical clustering

Contact Info

Product

Resources

About