Zobrazeno 1 - 10
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pro vyhledávání: '"Shun, Julian"'
Many real-world graphs frequently present challenges for graph learning due to the presence of both heterophily and heterogeneity. However, existing benchmarks for graph learning often focus on heterogeneous graphs with homophily or homogeneous graph
Externí odkaz:
http://arxiv.org/abs/2407.10916
We define and investigate the problem of $\textit{c-approximate window search}$: approximate nearest neighbor search where each point in the dataset has a numeric label, and the goal is to find nearest neighbors to queries within arbitrary label rang
Externí odkaz:
http://arxiv.org/abs/2402.00943
Many important societal problems are naturally modeled as algorithms over temporal graphs. To date, however, most graph processing systems remain inefficient as they rely on distributed processing even for graphs that fit well within a commodity serv
Externí odkaz:
http://arxiv.org/abs/2401.02563
This paper studies density-based clustering of point sets. These methods use dense regions of points to detect clusters of arbitrary shapes. In particular, we study variants of density peaks clustering, a popular type of algorithm that has been shown
Externí odkaz:
http://arxiv.org/abs/2312.03940
The densest subgraph problem has received significant attention, both in theory and in practice, due to its applications in problems such as community detection, social network analysis, and spam detection. Due to the high cost of obtaining exact sol
Externí odkaz:
http://arxiv.org/abs/2311.04333
Nucleus decompositions have been shown to be a useful tool for finding dense subgraphs. The coreness value of a clique represents its density based on the number of other cliques it is adjacent to. One useful output of nucleus decomposition is to gen
Externí odkaz:
http://arxiv.org/abs/2306.08623
Clustering multidimensional points is a fundamental data mining task, with applications in many fields, such as astronomy, neuroscience, bioinformatics, and computer vision. The goal of clustering algorithms is to group similar objects together. Dens
Externí odkaz:
http://arxiv.org/abs/2305.11335
Autor:
Yu, Shangdi, Shun, Julian
Given all pairwise weights (distances) among a set of objects, filtered graphs provide a sparse representation by only keeping an important subset of weights. Such graphs can be passed to graph clustering algorithms to generate hierarchical clusters.
Externí odkaz:
http://arxiv.org/abs/2303.05009
Autor:
Dhulipala, Laxman, Liu, Quanquan C., Raskhodnikova, Sofya, Shi, Jessica, Shun, Julian, Yu, Shangdi
Differentially private algorithms allow large-scale data analytics while preserving user privacy. Designing such algorithms for graph data is gaining importance with the growth of large networks that model various (sensitive) relationships between in
Externí odkaz:
http://arxiv.org/abs/2211.10887