An optimization-based approach to node role discovery in networks: approximating equitable partitions
Similar to community detection, partitioning the nodes of a complex network according to
their structural roles aims to identify fundamental building blocks of a network, which can be …
their structural roles aims to identify fundamental building blocks of a network, which can be …
Multi-scale Wasserstein Shortest-path Graph Kernels for Graph Classification
W Ye, H Tian, Q Chen - IEEE Transactions on Artificial …, 2023 - ieeexplore.ieee.org
Graph kernels are conventional methods for computing graph similarities. However, the
existing R-convolution graph kernels cannot resolve both of the two challenges: 1) …
existing R-convolution graph kernels cannot resolve both of the two challenges: 1) …
Exploring Consistency in Graph Representations: from Graph Kernels to Graph Neural Networks
Graph Neural Networks (GNNs) have emerged as a dominant approach in graph
representation learning, yet they often struggle to capture consistent similarity relationships …
representation learning, yet they often struggle to capture consistent similarity relationships …
Deep Hierarchical Graph Alignment Kernels
S Tang, H Tian, X Cao, W Ye - arxiv preprint arxiv:2405.05545, 2024 - arxiv.org
Typical R-convolution graph kernels invoke the kernel functions that decompose graphs into
non-isomorphic substructures and compare them. However, overlooking implicit similarities …
non-isomorphic substructures and compare them. However, overlooking implicit similarities …
Towards Subgraph Isomorphism Counting with Graph Kernels
Subgraph isomorphism counting is known as# P-complete and requires exponential time to
find the accurate solution. Utilizing representation learning has been shown as a promising …
find the accurate solution. Utilizing representation learning has been shown as a promising …
Enhancing Shortest-Path Graph Kernels via Graph Augmentation
The conventional shortest-path graph kernel (SP) decomposes graphs into shortest paths
and computes their frequencies in each graph. However, SP cannot compare graphs with …
and computes their frequencies in each graph. However, SP cannot compare graphs with …
Utilizing Constrained Homomorphisms in the Design of Efficient Graph Kernels
TH Schulz - 2024 - bonndoc.ulb.uni-bonn.de
Learning on graphs, particularly graph classification, requires rich graph representations. A
common paradigm to obtain these is by extracting sets of substructures and representing …
common paradigm to obtain these is by extracting sets of substructures and representing …