1 Introduction Estimating the number of triangles in a graph is a fundamental problem and has found applications in many fields.For example,in social network,it can help us understand how closely the local community s...1 Introduction Estimating the number of triangles in a graph is a fundamental problem and has found applications in many fields.For example,in social network,it can help us understand how closely the local community structure and nodes in the network are in close proximity.In this paper,we address this problem in the framework of graph streaming algorithms,which has received significant attention due to the increasing need to analyze large-scale graph data efficiently[1–3].However,most of these algorithms are not robust or are limited to unweighted graphs.展开更多
基金supported in part by the Innovation Program for Quantum Science and Technology(No.2021ZD0302901)in part by the National Natural Science Foundation of China(Grant No.62272431).
文摘1 Introduction Estimating the number of triangles in a graph is a fundamental problem and has found applications in many fields.For example,in social network,it can help us understand how closely the local community structure and nodes in the network are in close proximity.In this paper,we address this problem in the framework of graph streaming algorithms,which has received significant attention due to the increasing need to analyze large-scale graph data efficiently[1–3].However,most of these algorithms are not robust or are limited to unweighted graphs.
