Considering weights in real social networks: A review

Bellingeri, Michele; Bevacqua, Danièle; Sartori, F.; Turchetto, Massimiliano; Scotognella, Francesco; Alfieri, Roberto; Nguyen, Ngoc-Kim-Khanh; Le, Thao-Giang; Nguyen, Quan; Cassi, Davide

doi:10.3389/fphy.2023.1152243

Cited by 7 publications

(7 citation statements)

References 126 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For this, network science research focusing on node attack strategies must consider that applying WT may significantly change the node attack efficacy. (II) Finding the best attack strategies is a heuristic way to select important nodes in the network [35]. Here, we show that WT performed with strong link removal changes the efficacy of the attack strategies.…”

Section: The Efficacy Of the Node Attack Strategiesmentioning

confidence: 72%

“…This last result has two important consequences. (I) Finding the best node attack strategies in real-world networks is a fundamental problem in network science with many real applications [31,35,37,38]. The WT procedure aiming to simplify the network by reducing the number of links induces structural changes that affect the efficacy of the node attack strategies.…”

Section: The Efficacy Of the Node Attack Strategiesmentioning

confidence: 99%

“…While computing shortest paths, it is fundamental to consider whether the link weight corresponds to "flows" or "costs" [35]. If link weight means flow, then the shortest path is computed by summing the inverse of link weights.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Robustness of Real-World Networks after Weight Thresholding with Strong Link Removal

John,

Bellingeri,

Lekha

et al. 2024

Mathematics

View full text Add to dashboard Cite

Weight thresholding (WT) is a method intended to decrease the number of links within weighted networks that may otherwise be excessively dense for network science applications. WT aims to remove links to simplify the network by holding most of the features of the original network. Here, we test the robustness and the efficacy of the node attack strategies on real-world networks subjected to WT that remove links of higher weight (strong links). We measure the network robustness along node removal with the largest connected component (LCC). We find that the real-world networks under study are generally robust when subjected to WT. Nonetheless, WT with strong link removal changes the efficacy of the attack strategies and the rank of node centralities. Also, WT with strong link removal may trigger a more significant change in the node centrality rank than WT by removing weak links. Network science research with the aim to find important/influential nodes in the network has to consider that simplifying the network with WT methodologies may change the node centrality.

show abstract

Section: The Efficacy Of the Node Attack Strategiesmentioning

confidence: 72%

Section: The Efficacy Of the Node Attack Strategiesmentioning

confidence: 99%

See 1 more Smart Citation

Robustness of Real-World Networks after Weight Thresholding with Strong Link Removal

John,

Bellingeri,

Lekha

et al. 2024

Mathematics

View full text Add to dashboard Cite

show abstract

“…By default, we use the q value in Equation (3) as the quality function. Note that the cohesion value of Equation (11) can also be calculated separately for the two factions of each division.…”

Section: Algorithm For Detecting Communitiesmentioning

confidence: 99%

“…When modelling network structures [10], it is important to describe the network's topology in terms of nodes and the connections between them. These connections can be represented as directed links between neighbouring nodes, and they have weights [8,11] that indicate their ability to convey information or influence. When modelling the spread of information or influence, we also have to consider network flow models.…”

Section: Introductionmentioning

confidence: 99%

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

Cluster synchronization of stochastic two‐layer networks with infinite distributed delays via delayed pinning impulsive control

Zhang,

Wei,

Wang

et al. 2024

Adaptive Control & Signal

View full text Add to dashboard Cite

SummaryThe cluster synchronization problem of stochastic two‐layer networks with infinite distributed delays is concerned. Firstly, we study the cluster synchronization of the first layer (leader‐layer) network with the average state of each cluster of sets as synchronization target. Secondly, we design a delayed pinning impulsive controller to synchronize the second layer (follower‐layer) network to the first layer network in a mean square cluster sense. Based on stochastic impulsive analysis and Lyapunov stability theory, some sufficient conditions for cluster synchronization are obtained. Finally, the effectiveness of the theoretical results is verified through numerical simulations.

show abstract

Considering weights in real social networks: A review

Cited by 7 publications

References 126 publications

Robustness of Real-World Networks after Weight Thresholding with Strong Link Removal

Robustness of Real-World Networks after Weight Thresholding with Strong Link Removal

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

Cluster synchronization of stochastic two‐layer networks with infinite distributed delays via delayed pinning impulsive control

Contact Info

Product

Resources

About