Abstract: Integrating Large Language Models (LLMs) with Graph Neural Networks (GNNs) has emerged as a dominant paradigm for representation learning on Text-Attributed Graphs (TAGs). Existing ...

The 2024 Nobel Prize in Chemistry was recently granted to David Baker, Demis Hassabis and John M. Jumper, renowned for their pioneering works in protein design. In addition, Nature has recently ...

Graphs are everywhere. From technology to finance, they often model valuable information such as people, networks, biological pathways and more. Often, scientists and technologists need to come up ...

Discrete Morse theory is a versatile tool from combinatorial algebraic topology. In a nutshell, discrete Morse theory uses certain well-behaved functions, the so-called discrete Morse functions, from ...

ABSTRACT: Let G=( V,E ) be a graph. The first Zagreb index of a graph G is defined as ∑ u∈V d G 2 ( u ) , where d G ( u ) is the degree of vertex u in G . In this paper, we obtain two lower bounds ...

Abstract: This paper presents a novel approach to graph learning, GL-AR, which leverages estimated autoregressive coefficients to recover undirected graph structures from time-series graph signals ...

ABSTRACT: In this paper, we consider chessboard graphs in higher dimensions and the number of edges of their corresponding graphs. First, we solve for the number of edges for some of the chessboard ...

Monograph's in-depth journey delves into the soul, revealing the essence of a subject with precision and passion. Monograph's in-depth journey delves into the soul, revealing the essence of a subject ...

High sparse Knowledge Graph is a key challenge to solve the Knowledge Graph Completion task. Due to the sparsity of the KGs, there are not enough first-order neighbors to learn the features of ...