Zobrazeno 1 - 10
of 943
pro vyhledávání: '"Kriege, A."'
The ability of message-passing neural networks (MPNNs) to fit complex functions over graphs is limited as most graph convolutions amplify the same signal across all feature channels, a phenomenon known as rank collapse, and over-smoothing as a specia
Externí odkaz:
http://arxiv.org/abs/2409.11504
Graph Neural Networks (GNNs) excel in handling graph-structured data but often underperform in link prediction tasks compared to classical methods, mainly due to the limitations of the commonly used Message Passing GNNs (MPNNs). Notably, their abilit
Externí odkaz:
http://arxiv.org/abs/2408.12334
The graph edit distance is used for comparing graphs in various domains. Due to its high computational complexity it is primarily approximated. Widely-used heuristics search for an optimal assignment of vertices based on the distance between local su
Externí odkaz:
http://arxiv.org/abs/2312.04123
Prior attacks on graph neural networks have mostly focused on graph poisoning and evasion, neglecting the network's weights and biases. Traditional weight-based fault injection attacks, such as bit flip attacks used for convolutional neural networks,
Externí odkaz:
http://arxiv.org/abs/2311.01205
Message passing neural networks iteratively generate node embeddings by aggregating information from neighboring nodes. With increasing depth, information from more distant nodes is included. However, node embeddings may be unable to represent the gr
Externí odkaz:
http://arxiv.org/abs/2310.04190
The H-index of a node in a static network is the maximum value $h$ such that at least $h$ of its neighbors have a degree of at least $h$. Recently, a generalized version, the $n$-th order H-index, was introduced, allowing to relate degree centrality,
Externí odkaz:
http://arxiv.org/abs/2305.16001
We propose improved exact and heuristic algorithms for solving the maximum weight clique problem, a well-known problem in graph theory with many applications. Our algorithms interleave successful techniques from related work with novel data reduction
Externí odkaz:
http://arxiv.org/abs/2302.00458
Autor:
Bause, Franka, Kriege, Nils M.
The classical Weisfeiler-Leman algorithm aka color refinement is fundamental for graph learning with kernels and neural networks. Originally developed for graph isomorphism testing, the algorithm iteratively refines vertex colors. On many datasets, t
Externí odkaz:
http://arxiv.org/abs/2209.09048
We introduce the \emph{temporal graphlet kernel} for classifying dissemination processes in labeled temporal graphs. Such dissemination processes can be spreading (fake) news, infectious diseases, or computer viruses in dynamic networks. The networks
Externí odkaz:
http://arxiv.org/abs/2209.07332
Autor:
Kriege, Nils M.
Random walk kernels have been introduced in seminal work on graph learning and were later largely superseded by kernels based on the Weisfeiler-Leman test for graph isomorphism. We give a unified view on both classes of graph kernels. We study walk-b
Externí odkaz:
http://arxiv.org/abs/2205.10914