IEEE

Enhancing Negotiation Policies via Spatio-Temporal Directed Graphs for Autonomous Interaction

Abstract: Mixed traffic environments, comprising autonomous vehicles (AVs) and human-driven vehicles (HDVs), present substantial challenges for developing negotiation policies. These policies are ...

Researchers Propose a Novel Representation Learning Framework to Address the Lack of Causal Characterization in Deep Learning Models

Recently, researchers introduced a new representation learning framework that integrates causal inference with graph neural networks—CauSkelNet, which can be used to model the causal relationships and ...
IEEE

Graph Model for Conflict Resolution for Mixed-Stability Combinatorial Foresight Based on the Combination of Regret Theory and VIKOR Method

Abstract: The graph model for conflict resolution (GMCR) is a branch of decision-making; therefore, how to scientifically abstract the evaluative thinking of decision-makers (DMs) has always been a ...
C&EN

Development of Coarse-Grained Lipid Force Fields Based on a Graph Neural Network

Department of Materials Science and Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong China Department of Physics, City University of Hong Kong, Kowloon 999077, Hong Kong China ...
GitHub

GraphTranslator: Aligning Graph Model to Large Language Model for Open-ended Tasks

Pre-training Graph Model Phase. In the pre-training phase, we employ link prediction as the self-supervised task for pre-training the graph model. Producer Phase. In the Producer phase, we employ LLM ...
GitHub

VisionGraph: Leveraging Large Multimodal Models for Graph Theory Problems in Visual Context

This repository contains the official implementation of our ICML 2024 paper, VisionGraph: Leveraging Large Multimodal Models for Graph Theory Problems in Visual Context. VisionGraph, is a benchmark ...