SDSC, skitter (July 1998) A random graph model for massive graphs William Aiello Fan Chung Graham Lincoln Lu What are thep(p)
A Random Graph Model for Massive Graphs William Aiello AT&T Labs Florham Park, New Jersey Fan Chung University of California, San Diego Linyuan Lu University of Pennsylvania, Philadelphia aiello@research.att.com fan@ucsd.edu llu@euclid.ucsd.edu ABSTRACT We propose a random graph model which is ...
The degree distribution of the graph of telephone calls seems to follow a power law as well; motivated by this, Aiello, Chung and Lu =-=[1]-=- proposed a model for ‘massive graphs’. This model ensures that the degree distribution follows a power law by fixing a degree sequence in ...
In step 1, using random noise variables we generate initialization data, which is fed into the root nodes of the graphs and propagated through the computational graph for each to-be-generated sample. In step 2, we randomly sample feature and target node positions in the graph, labelled F ...
Recently, data structures like graphs have been recognized as one of convenient and intuitive ways to represent residues in a protein, and their interactions [17]. Mahbub et al. [18] proposed an EGRET model, which introduces graph edge features based on graph attention networks (GAT) [19], ...
knowledge graphs;ontologies;graph databases;data modeling;dementia;omics 1. Introduction The dawn of “omics” technologies, accompanied by advancements in imaging, clinical data collection, laboratory testing, and phenotyping, has profoundly influenced biomedical research [1,2,3,4,5,6,7]. This multi...
using random noise variables we generate initialization data, which is fed into the root nodes of the graphs and propagated through the computational graph for each to-be-generated sample. In step 2, we randomly sample feature and target node positions in the graph, labelled F and T, respectiv...
Graph neural networks (GNNs) have been used previously for identifying new crystalline materials. However, geometric structure is not usually taken into consideration, or only partially. Here, we develop a geometric-information-enhanced crystal graph neu
experiments, both “MMGraph + GL1” and “MMGraph + GL2” demonstrated performance improvements over MMGraph on most metrics, illustrating that introducing attention mechanisms at both the first and second layers can effectively learn node embeddings in heterogeneous graphs, thereby enhancing ...
Big Data frameworks provide support for managing massive amounts of data in reasonable time by using clusters of distributed nodes. Similarly, parallel algorithms, enabling concurrent processing of graph sections, allow us to efficiently deal with large graphs. In this work we have focused on the ...