Complex Network Metrics: Can Deep Learning Keep up With Tailor-Made Reference Algorithms?

Wandelt, Sebastian; Shi, Xing; Sun, Xiaoqian

doi:10.1109/access.2020.2984762

Cited by 11 publications

(4 citation statements)

References 39 publications

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“…As for step b) , we note that the computation of the degree is linear in the number of edges and, therefore, it takes O ( m ′) time. The eigenvector can be computed by using power iteration method [ 41 ] in O ( n + m ) time while the traditional methods to compute the Katz centrality takes O ( n 3 ) [ 42 ].…”

Section: Discussionmentioning

confidence: 99%

On the sensitivity of centrality metrics

Cavallaro,

De Meo,

Fiumara

et al. 2024

PLoS ONE

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Despite the huge importance that the centrality metrics have in understanding the topology of a network, too little is known about the effects that small alterations in the topology of the input graph induce in the norm of the vector that stores the node centralities. If so, then it could be possible to avoid re-calculating the vector of centrality metrics if some minimal changes occur in the network topology, which would allow for significant computational savings. Hence, after formalising the notion of centrality, three of the most basic metrics were herein considered (i.e., Degree, Eigenvector, and Katz centrality). To perform the simulations, two probabilistic failure models were used to describe alterations in network topology: Uniform (i.e., all nodes can be independently deleted from the network with a fixed probability) and Best Connected (i.e., the probability a node is removed depends on its degree). Our analysis suggests that, in the case of degree, small variations in the topology of the input graph determine small variations in Degree centrality, independently of the topological features of the input graph; conversely, both Eigenvector and Katz centralities can be extremely sensitive to changes in the topology of the input graph. In other words, if the input graph has some specific features, even small changes in the topology of the input graph can have catastrophic effects on the Eigenvector or Katz centrality.

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Section: Discussionmentioning

confidence: 99%

On the sensitivity of centrality metrics

Cavallaro,

De Meo,

Fiumara

et al. 2024

PLoS ONE

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“…Xu et al (2020) proposed an important node identification algorithm based on the information entropy of node adjacency. Wandelt et al (2020) applied deep learning to node importance ranking, accelerating the important node identification process compared with other measurement methods. Yu et al (2020) proposed a simple and effective method to identify key nodes, considering the adjacency matrix of the network and convolutional neural network.…”

Section: Node Importance Rankingmentioning

confidence: 99%

A Novel Compressed Sensing-Based Graph Isomorphic Network for Key Node Recognition and Entity Alignment

Zhao

Huang

Fan

et al. 2022

International Journal on Semantic Web and Information Systems

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In recent years, the related research of entity alignment has mainly focused on entity alignment via knowledge embeddings and graph neural networks; however, these proposed models usually suffer from structural heterogeneity and the large-scale problem of knowledge graph. A novel entity alignment model based on graph isomorphic network and compressed sensing is proposed. First, for the problem of structural heterogeneity, graph isomorphic network encoder is applied in knowledge graph to capture structural similarity of entity relation. Second, for the problem of large scale, key node and community are integrated for priority entity alignment to improve execution speed. However, the exiting node importance ranking algorithm cannot accurately identify key node in knowledge graph. So the compressed sensing is adopted in node importance ranking to improve the accuracy of identifying key node. The authors have carried out several experiments to test the effect and efficiency of the proposed entity alignment model.

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“… measures the physical centrality of a node because a more central node is necessarily closer to all other nodes. Efficient algorithms require computation steps, in the worst case, to calculate the exact betweenness and closeness centralities for the whole graphs by conducting a breadth-first search from each node 51 . The eigenvector centrality estimates the importance of a node based on that of its neighbours.…”

Section: Proposed Approachmentioning

confidence: 99%

“… is defined as the element of the vector that is solution to the equation , where is the adjacency matrix of the graph and the largest eigenvalue associated with the eigenvector of A . This eigenvector can be computed by the power iteration method in time 51 . The eccentricity ecc of a node, on the other hand, is a measure of non-centrality defined as the maximum distance from a given node to any other node in the graph.…”

Section: Proposed Approachmentioning

confidence: 99%

Topological network features determine convergence rate of distributed average algorithms

Sirocchi

Bogliolo²

2022

Sci Rep

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Gossip algorithms are message-passing schemes designed to compute averages and other global functions over networks through asynchronous and randomised pairwise interactions. Gossip-based protocols have drawn much attention for achieving robust and fault-tolerant communication while maintaining simplicity and scalability. However, the frequent propagation of redundant information makes them inefficient and resource-intensive. Most previous works have been devoted to deriving performance bounds and developing faster algorithms tailored to specific structures. In contrast, this study focuses on characterising the effect of topological network features on performance so that faster convergence can be engineered by acting on the underlying network rather than the gossip algorithm. The numerical experiments identify the topological limiting factors, the most predictive graph metrics, and the most efficient algorithms for each graph family and for all graphs, providing guidelines for designing and maintaining resource-efficient networks. Regression analyses confirm the explanatory power of structural features and demonstrate the validity of the topological approach in performance estimation. Finally, the high predictive capabilities of local metrics and the possibility of computing them in a distributed manner and at a low computational cost inform the design and implementation of a novel distributed approach for predicting performance from the network topology.

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Complex Network Metrics: Can Deep Learning Keep up With Tailor-Made Reference Algorithms?

Cited by 11 publications

References 39 publications

On the sensitivity of centrality metrics

On the sensitivity of centrality metrics

A Novel Compressed Sensing-Based Graph Isomorphic Network for Key Node Recognition and Entity Alignment

Topological network features determine convergence rate of distributed average algorithms

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