Zobrazeno 1 - 10
of 75 754
pro vyhledávání: '"GRAPH REPRESENTATION"'
Autor:
Zhang, Jianqiang1 (AUTHOR) zhangjianq@cug.edu.cn, Chen, Renyao1 (AUTHOR) cryao@cug.edu.cn, Li, Shengwen1,2,3 (AUTHOR) swli@cug.edu.cn, Li, Tailong4 (AUTHOR) ltl@cug.edu.cn, Yao, Hong1,2,3,4 (AUTHOR) yaohong@cug.edu.cn
Publikováno v:
Algorithms. Dec2024, Vol. 17 Issue 12, p593. 16p.
Graph representation learning methods are highly effective in handling complex non-Euclidean data by capturing intricate relationships and features within graph structures. However, traditional methods face challenges when dealing with heterogeneous
Externí odkaz:
http://arxiv.org/abs/2412.08038
Graph Transformers (GTs) have demonstrated significant advantages in graph representation learning through their global attention mechanisms. However, the self-attention mechanism in GTs tends to neglect the inductive biases inherent in graph structu
Externí odkaz:
http://arxiv.org/abs/2412.02285
Autor:
Hua, Chenqing
Graphs serve as fundamental descriptors for systems composed of interacting elements, capturing a wide array of data types, from molecular interactions to social networks and knowledge graphs. In this paper, we present an exhaustive review of the lat
Externí odkaz:
http://arxiv.org/abs/2411.07269
We introduce a representation of $[[n, k]]$ stabilizer codes as semi-bipartite graphs wherein $k$ ``input'' nodes map to $n$ ``output'' nodes, such that output nodes may connect to each other but input nodes may not. We prove that this graph represen
Externí odkaz:
http://arxiv.org/abs/2411.14448
Publikováno v:
ECAI 2023. IOS Press, 2023. 629-636
Self-supervised graph representation learning (SSGRL) is a representation learning paradigm used to reduce or avoid manual labeling. An essential part of SSGRL is graph data augmentation. Existing methods usually rely on heuristics commonly identifie
Externí odkaz:
http://arxiv.org/abs/2412.18316
Real-world graph data environments intrinsically exist noise (e.g., link and structure errors) that inevitably disturb the effectiveness of graph representation and downstream learning tasks. For homogeneous graphs, the latest works use original node
Externí odkaz:
http://arxiv.org/abs/2412.18267
While message passing graph neural networks result in informative node embeddings, they may suffer from describing the topological properties of graphs. To this end, node filtration has been widely used as an attempt to obtain the topological informa
Externí odkaz:
http://arxiv.org/abs/2412.17468
Autor:
Jin, Yihong, Yang, Ze
The detection of scams within Ethereum smart contracts is a critical challenge due to their increasing exploitation for fraudulent activities, leading to significant financial and reputational damages. Existing detection methods often rely on contrac
Externí odkaz:
http://arxiv.org/abs/2412.12370