Search Results for author:
Found 14 papers, 7 papers with code
Weisfeiler and Leman Follow the Arrow of Time: Expressive Power of Message Passing in Temporal Event Graphs
To formally analyze the expressive power of TGNNs, we lack a generalization of graph isomorphism to temporal graphs that fully captures their causal topology.
A Customized SAT-based Solver for Graph Coloring
Our method is based on an approach of H\'ebrard and Katsirelos (2020) that employs a propagator to enforce transitivity constraints, incorporate lower bounds for search tree pruning, and enable inferred propagations.
SAT Encoding of Partial Ordering Models for Graph Coloring Problems
For the widely studied GCP, we experimentally compare our new SAT encoding to the state-of-the-art approaches on the DIMACS benchmark set.
A Temporal Graphlet Kernel for Classifying Dissemination in Evolving Networks
We introduce the \emph{temporal graphlet kernel} for classifying dissemination processes in labeled temporal graphs.
Temporal Walk Centrality: Ranking Nodes in Evolving Networks
We propose the Temporal Walk Centrality, which quantifies the importance of a node by measuring its ability to obtain and distribute information in a temporal network.
TUDataset: A collection of benchmark datasets for learning with graphs
We provide Python-based data loaders, kernel and graph neural network baseline implementations, and evaluation tools.
Temporal Graph Kernels for Classifying Dissemination Processes
Hence, we confirm that taking temporal information into account is crucial for the successful classification of dissemination processes.
Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings
Hence, it accounts for the higher-order interactions between vertices.
Ranked #4 on
Graph Classification
on NCI109
Recognizing Cuneiform Signs Using Graph Based Methods
To this end, the distance measure is used to implement a nearest neighbor classifier leading to a high computational cost for the prediction phase with increasing training set size.
Global Weisfeiler-Lehman Graph Kernels
Specifically, we introduce a novel graph kernel based on the $k$-dimensional Weisfeiler-Lehman algorithm.
A Unifying View of Explicit and Implicit Feature Maps of Graph Kernels
On this basis we propose exact and approximative feature maps for widely used graph kernels based on the kernel trick.
Faster Kernels for Graphs with Continuous Attributes via Hashing
While state-of-the-art kernels for graphs with discrete labels scale well to graphs with thousands of nodes, the few existing kernels for graphs with continuous attributes, unfortunately, do not scale well.
StruClus: Structural Clustering of Large-Scale Graph Databases
We present a structural clustering algorithm for large-scale datasets of small labeled graphs, utilizing a frequent subgraph sampling strategy.
Subgraph Matching Kernels for Attributed Graphs
To compute the kernel we propose a graph-theoretical algorithm inspired by a classical relation between common subgraphs of two graphs and cliques in their product graph observed by Levi (1973).
