Search Results for author:
Found 5 papers, 3 papers with code
Structure-aware Semantic Node Identifiers for Learning on Graphs
We present a novel graph tokenization framework that generates structure-aware, semantic node identifiers (IDs) in the form of a short sequence of discrete codes, serving as symbolic representations of nodes.
Decoy Effect In Search Interaction: Understanding User Behavior and Measuring System Vulnerability
This study examines the decoy effect's underexplored influence on user search interactions and methods for measuring information retrieval (IR) systems' vulnerability to this effect.
Improving Self-supervised Molecular Representation Learning using Persistent Homology
Self-supervised learning (SSL) has great potential for molecular representation learning given the complexity of molecular graphs, the large amounts of unlabelled data available, the considerable cost of obtaining labels experimentally, and the hence often only small training datasets.
Enhancing Graph Transformers with Hierarchical Distance Structural Encoding
Empirically, we demonstrate that graph transformers with HDSE excel in graph classification, regression on 7 graph-level datasets, and node classification on 11 large-scale graphs, including those with up to a billion nodes.
Ranked #2 on
Graph Classification
on CIFAR10 100k
Impact-Oriented Contextual Scholar Profiling using Self-Citation Graphs
Experiment outcomes show that the F1 score of best GF profile significantly outperforms alternative methods of impact indicators and bibliometric networks in all the 6 computer science fields considered.
