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发表于 2025-3-28 15:16:54
2213-8684and findings about all types of networks, from biological, to technological, to informational and social. It is this interdisciplinary nature of complex networks that CompleNet aims to explore and celebrate.. .978-3-030-40945-6978-3-030-40943-2Series ISSN 2213-8684 Series E-ISSN 2213-8692
modifier
发表于 2025-3-28 20:05:26
Condensed Graphs: A Generic Framework for Accelerating Subgraph Census Computationdistributions. Current state-of-the-art algorithms for this task either take advantage of common patterns emerging on the networks or target a set of specific subgraphs for which analytical calculations are feasible. Here, we propose a novel network generic framework revolving around a new data-stru
钢笔记下惩罚
发表于 2025-3-29 00:03:50
Group Cohesion Assessment in Networkso establish the level of cohesion among network nodes. It comes from the Black-Hole metric introduced as a solution of the normalization problem that affects PageRank; in particular, here we present an extension that leverages a set of black hole nodes to assess intra- and inter-group cohesion in pa
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发表于 2025-3-29 03:14:57
Node Classification with Bounded Error Ratesh to associate a confidence level with a prediction such that the error in the prediction is guaranteed. We propose adopting the Conformal Prediction framework [.] to obtain guaranteed error bounds in node classification problem. We show how this framework can be applied to (1) obtain predictions wi
DIKE
发表于 2025-3-29 08:07:38
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harrow
发表于 2025-3-29 12:51:16
Unsupervised Strategies to Network Topology Reconfiguration Optimization with Limited Link Additionange aiming to bring the properties to an acceptable range is called . (NTRLA). We faced an NTRLA problem when we were investigating ways to improve the efficiency of large power grids. In the search for solutions, we developed strategies to add new edges in unsupervised automatic applications. The
胰脏
发表于 2025-3-29 18:11:57
Embedding of Signed Networks Focusing on Both Structure and Relationetworks. Most of the research on network embedding are for simple networks without edge signs. However, relations among users in real social media are often represented as signed networks composed of positive and negative relationships. In this paper, we propose a new method for embedding signed net