Bipartite graph-based community-to-community matching in local energy market considering socially networked prosumers

Xia, Yuanxing; Xu, Qingshan; Fang, Jicheng; Tang, Rongchuan; Du, Pengwei

doi:10.1016/j.apenergy.2023.122245

Cited by 3 publications

(2 citation statements)

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“…N i is a set consisting of the indices of the K samples obtained by applying the KNN to z i . By minimizing Equation (11), similar samples are pushed closer together while dissimilar samples are pushed father apart. The objective of this minimization is to enhance the discrimination between samples in the global representation.…”

Section: Global Representation Enhancementmentioning

confidence: 99%

“…For instance, in biological networks, the interactions between proteins in some organisms may involve multiple interaction patterns among thousands of protein molecules, with each protein [10] having specific attribute information. In social networks [11], individuals may have different types of social relationships, such as friends, followers, and co-groups, with each individual possessing certain descriptive characteristics. In a transportation planning network, locations are abstracted as nodes, while different types of roads: subways, highways, railways, etc., are abstracted as sides of different views.…”

Section: Introductionmentioning

confidence: 99%

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Local-Global Representation Enhancement for Multi-View Graph Clustering

Zhao,

Hou,

Wang

2024

Electronics

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In recent years, multi-view graph clustering algorithms based on representations learning have received extensive attention. However, existing algorithms are still limited in two main aspects, first, most algorithms employ graph convolution networks to learn the local representations, but the presence of high-frequency noise in these representations limits the clustering performance. Second, in the process of constructing a global representation based on the local representations, most algorithms focus on the consistency of each view while ignoring complementarity, resulting in lower representation quality. To address the aforementioned issues, a local-global representation enhancement for multi-view graph clustering algorithm is proposed in this paper. First, the low-frequency signals in the local representations are enhanced by a low-pass graph encoder, which yields smoother and more suitable local representations for clustering. Second, by introducing an attention mechanism, the local embedded representations of each view can be weighted and fused to obtain a global representation. Finally, to enhance the quality of the global representation, it is jointly optimized using the neighborhood contrastive loss and reconstruction loss. The final clustering results are obtained by applying the k-means algorithm to the global representation. A wealth of experiments have validated the effectiveness and robustness of the proposed algorithm.

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Section: Global Representation Enhancementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%