Adaptive Modularity Maximization via Edge Weighting Scheme.

We also present a novel regression model which assigns weights to the edges of a graph according to their local topological features to enhance the accuracy of modularity maximization algorithms. ... Modularity maximization is one of the state-of-the-art methods for community detection that has gained popularity in the last decade. ... Figure 1 : 1 Illustration of the adaptive modularity maximization. • Edge feature extraction: extract topological features of the edges in the artificial graph. ...

doi:10.1016/j.ins.2017.09.063 fatcat:x4bdhl6vefglpc54jdskgzf35q

Multiple Versions

Optimized via a deterministic annealing scheme, this measure produces topologically ordered graph clusterings that lead to faithful and readable graph representations based on clustering induced graphs ... This paper proposes an organized generalization of Newman and Girvan's modularity measure for graph clustering. ... Those results show that the combination of modularity maximization and edge crossings minimization is very well adapted to graph visualization. ...

doi:10.1016/j.neucom.2009.11.023 fatcat:prj2k5qcmnbofdwycfmzeylrbm

Multiple Versions

Posterior computation proceeds via an efficient Markov Chain Monte Carlo algorithm. ... Simulations demonstrate that LCN outperforms several other competing methods for community detection in weighted networks, and we apply our RGMM to estimate the latent community structures in the functional ... in each network and estimate the modularity parameters governing the edge weights. ...

doi:10.4310/sii.2017.v10.n3.a1 pmid:29034059 pmcid:PMC5639930 fatcat:eirxxoufqfegtkpjcswhy37gvy

Szczepanski

While black-box meta-learning optimizes a general-purpose adaptation procedure via (stochastic) gradient descent, modular meta-learning finds a set of reusable modules that can form components of a solution ... The specific GNN architecture, known as random edge GNN (REGNN), defines a non-linear graph convolutional filter whose spatial weights are tied to the channel coefficients. ... The GNN encodes information about the network topology through its underlying graph whose edge weights are tied to the channel realizations. ...

arXiv:2108.13178v2 fatcat:sh2eadjoa5cznoiq7t3gvw4zj4

Multiple Versions

Given a weighted or unweighted network, we describe how to derive new edge weights in order to achieve a low ϵ, we modify the "CNM" community detection algorithm to maximize weighted modularity, and show ... We conclude that weighted modularity algorithms may fail to resolve communities with fewer than √(W ϵ/2) total edge weight, where W is the total edge weight in the network and ϵ is the maximum weight of ... Two challenges remain: finding a method to set edge weights that achieve a small , and adapting modularity maximization algorithms to use weights. ...

doi:10.1103/physreve.83.056119 pmid:21728617 fatcat:layj4yid4rf4jeptpviygqsdq4

Multiple Versions

This paper describes a graph visualization methodology based on hierarchical maximal modularity clustering, with interactive and significant coarsening and refining possibilities. ... Among the corresponding clustering methods, the ones that maximize the modularity of the obtained partition seem the more adapted: modularity maximization has been shown to produce interesting partitions ... [12] , not all of them are adapted to graph visualization. ...

arXiv:1210.5693v1 fatcat:xzpjrhb7evcjdkmvhsj2kspd5u

OPTIMIZATION OF A MODULARITY-BASED MEASURE Next, we present a new algorithm to extract the hierarchical structure that was developed by improving the scheme [38] via the signed network. ... Specifically, an algorithm for optimizing a modularity [33] -based measure, which can adaptively adjust the balance between positive and negative edges, was newly developed. ...

doi:10.1109/access.2017.2741098 fatcat:sdvynxvpuzgyxomw6rvoocs7gq

DOAJ

In this paper we apply the multilevel paradigm to the modularity graph clustering problem. ... Nerstrand works well on large graphs, clustering a graph with over 105 million vertices and 3.3 billion edges in 90 seconds. ... We adapted the FirstChoice aggregation scheme from graph partitioning, such that is able to maximize modularity. ...

doi:10.1016/j.jpdc.2014.09.012 fatcat:m7hrrenr5ne45mcbytbhq2h7e4

Unlike many previous modularity maximization strategies, which rely on some form of aggregation of the various layers, our multiobjective approach aims at maximizing the individual modularities on each ... search procedure in terms of a filter-based multiobjective optimization scheme. ... With this model, communities are found via the maximization of Q. ...

arXiv:2106.13543v2 fatcat:ep2owb66gfavdcwcalzghvvcy4

Multiple Versions

We find that WSBM communities exhibit greater hemispheric symmetry and are spatially less compact than those derived from modularity maximization. ... Of particular interest is the network's community structure, commonly identified by modularity maximization, where communities are conceptualized as densely intra-connected and sparsely inter-connected ... with high edge weight. ...

doi:10.1038/s41598-018-31202-1 pmid:30158553 fatcat:nzialssmj5ga5pehbgslads7x4

DOAJ

clustering property, then extracts the well-connected sub-sub-graph round each community seed as prior information to supplement symmetric adjacent matrix, and finally identifies precise communities via ... Notice that there exist two restrictions: (i) each edge increases weight at most once; (ii) the upper limit of edge weight equals to 1. ... the sum of weights of all edges in global network, d u expresses the sum of weights of edges incident on u, and o u counts the number of sub-graphs associating with u. ...

doi:10.3390/sym13050869 fatcat:bd64dzirtvdrliosag2fmatbri

DOAJ Szczepanski

Toward detecting communities in such bipartite networks, we make the following contributions: i) we define a variant of the bipartite modularity function defined by Murata to overcome one of its limitations ... Experimental results show that our biLouvain algorithm identifies communities that have a comparable or better quality (bipartite modularity) than existing methods, while significantly reducing the time-to-solution ... Effect of Edge Weights We studied the effect of introducing edge weights using the plant-pollinator networks. Figure 7 shows results of our analysis. ...

doi:10.1145/2975167.2975177 dblp:conf/bcb/Pesantez-Cabrera16 fatcat:h2i2ceablvgd5fzwcfzaq754x4

Toward detecting communities in such bipartite networks, we make the following contributions: i) (metric) we propose a variant of bipartite modularity; ii) (algorithms) we present an efficient algorithm ... Experimental results show that our biLouvain algorithm identifies communities that have a comparable or better quality (as measured by bipartite modularity) than existing methods, while significantly reducing ... For convenience we assume unweighted edges. We consider two possible cases to determine the optimal length of a chain for modularity maximization. ...

doi:10.1109/tcbb.2017.2765319 pmid:29990252 fatcat:d3k6piplcjfstlnquza4aeulji

Toward detecting communities in such bipartite networks, we make the following contributions: i) (metric) we propose a variant of bipartite modularity; ii) (algorithms) we present an efficient algorithm ... Experimental results show that our biLouvain algorithm identifies communities that have a comparable or better quality (as measured by bipartite modularity) than existing methods, while significantly reducing ... For convenience we assume unweighted edges. We consider two possible cases to determine the optimal length of a chain for modularity maximization. ...

doi:10.1101/105197 fatcat:qm63d32dqzevlbw353qvldr5qa

In this paper, a concept of robust enhancement is proposed for community detection, with two algorithms presented: one is named robust enhancement via genetic algorithm (RobustECD-GA), in which the modularity ... and the number of clusters are used to design a fitness function to solve the resolution limit problem; the other is called robust enhancement via similarity ensemble (RobustECD-SE), integrating multiple ... Therefore, based on modularity, we propose the first method, named robust enhancement of community detection via genetic algorithm (RobustECD-GA), which aims to optimize community structure via adaptable ...

arXiv:1911.01670v3 fatcat:zo53q5vdgzerfixhr5kaxvny2q

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Adaptive modularity maximization via edge weighting scheme

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