47 citations · 52 across the 7 of their papers we have counts for
7 papers
A Simple Active-Set Method for PageRank-Based Local Graph Clustering
Zhewei Wei, Mingji Yang
Local graph clustering aims to find a well-connected cluster near a given seed node without exploring the entire graph. A key step in the classic local clustering algorithm of Ande…
On Solving Asymmetric Diagonally Dominant Linear Systems in Sublinear Time
Tsz Chiu Kwok, Zhewei Wei, Mingji Yang
We initiate a study of solving a row/column diagonally dominant (RDD/CDD) linear system in sublinear time, with the goal of estimating for a given vector $t\in…
PageRank Centrality in Directed Graphs with Bounded In-Degree
Mikkel Thorup, Hanzhi Wang, Zhewei Wei +1
We study the computational complexity of locally estimating a node's PageRank centrality in a directed graph . For any node , its PageRank centrality is defined as the…
Very Large-Scale Multi-Agent Simulation in AgentScope
Xuchen Pan, Dawei Gao, Yuexiang Xie +6
Recent advances in large language models (LLMs) have opened new avenues for applying multi-agent systems in very large-scale simulations. However, there remain several challenges w…
Revisiting Local Computation of PageRank: Simple and Optimal
Hanzhi Wang, Zhewei Wei, Ji-Rong Wen +1
We revisit the classic local graph exploration algorithm ApproxContributions proposed by Andersen, Borgs, Chayes, Hopcroft, Mirrokni, and Teng (WAW '07, Internet Math. '08) for com…
Efficient Algorithms for Personalized PageRank Computation: A Survey
Mingji Yang, Hanzhi Wang, Zhewei Wei +2
Personalized PageRank (PPR) is a traditional measure for node proximity on large graphs. For a pair of nodes and , the PPR value equals the probability that an -…
Approximating Single-Source Personalized PageRank with Absolute Error Guarantees
Zhewei Wei, Ji-Rong Wen, Mingji Yang
Personalized PageRank (PPR) is an extensively studied and applied node proximity measure in graphs. For a pair of nodes and on a graph , the PPR value is…