Results 111 to 120 of about 617,805 (269)
Forced Multi-Agent Bipartite Consensus Control: Application to Quadrotor Formation Flying
IEEE AccessThis work investigates the multi-agent bipartite consensus problem in the presence of switching antagonistic interactions. For leaderless multi-agent systems communicating over a structurally balanced sign graph with switching antagonistic interactions ...
Jose A. Guerrero +3 more
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Multi-view Clustering Based on Bipartite Graph Cross-view Graph Diffusion [PDF]
Jisuanji kexueMulti-view clustering is an research hotspots in the field of unsupervised learning.Recently,the method based on cross-view graph diffusion uses the complementary information between multiple views to obtain a unified graph for clustering on the basis of
WANG Jinfu, WANG Siwei, LIANG Weixuan, YU Shengju, ZHU En
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ADAPT: An Autonomous Forklift for Construction Site Operation
Journal of Field Robotics, EarlyView.ABSTRACT Efficient material logistics are critical in controlling costs and schedules in the construction industry. However, manual material handling remains prone to inefficiencies, delays, and safety risks. Autonomous forklifts offer a solution to streamline on‐site logistics, reducing reliance on human operators and mitigating labor shortages.
Johannes Huemer +12 more
wiley +1 more source
A Note on the Existence of All (g,f)-Factors
Journal of Graph Algorithms and Applications, 2017 This paper is devoted to the problem of existence of all $(g,f)$-factors in a bipartite graph. We present an algorithm to test if a given bipartite graph contains all $(g,f)$-factors.
Radosław Cymer
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On the bipartite independence number of a balanced bipartite graph [PDF]
, 1993 The bipartite independence number αBIP of a bipartite graph G is the maximum order of a balanced independent set of G. Let δ be the minimum degree of the graph.
Mago, Pedro +2 more
core +1 more source
A Bipartite Graph That Is Not the $\gamma$-Graph of a Bipartite Graph
, 2020 Comment: 4 ...
openaire +1 more source
Generating Compressed Counterfactual Hard Negative Samples for Graph Contrastive Learning
CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT Graph contrastive learning (GCL) relies on acquiring high‐quality positive and negative samples to learn the structural semantics of the input graph. Previous approaches typically sampled negative samples from the same training batch or an irrelevant external graph.
Haoran Yang +7 more
wiley +1 more source
Relations between ordinary energy and energy of a self-loop graph
HeliyonLet G be a graph on n vertices with vertex set V(G) and let S⊆V(G) with |S|=α. Denote by GS, the graph obtained from G by adding a self-loop at each of the vertices in S.
B.R. Rakshith +3 more
doaj +1 more source