当我们分析计算机网络或者社交网络这些graph 的时候,我们需要用一个人为生成graph 模拟真实的网络。所以我们第一步需要检测这些真实的网络中存在哪些模型(patterns)然后才能去模拟。这篇笔记主要是记录学习patterns in graph 这里有许多问题:我们怎么区分不同的模型;怎么衡量不同模型之间的相似度,因为我们需要将我们的生成...
另外KaHIP也实现了一系列基于层次化分割的算法。 [1] I. Stanton and G. Kliot. Streaming graph partitioning for large distributed graphs. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’12, pages 1222–1230, New York, NY, USA, 2012. ...
[6].Yu W, Cheng W, Aggarwal C C, et al. NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic Networks[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2018: 2672-2681. [7].Aditya Grover and Jure Leskovec...
There are plenty of mining industry narratives that fit into AI or whatever the next trend is – humanoid robots most likely. Uranium for one,dragged downby the Deepseek debacle because supposedly fewer small modular reactors will now be needed to power data centres. ...
Graph mining is one of the most important areas in data mining. However, scalable solutions for graph mining are still lacking as existing studies focus on sequential algorithms. While many distributed graph processing systems have been proposed in recent years, most of them were designed to parall...
The graph edit distance is an intuitive measure to quantify the dissimilarity of graphs, but its computation is $$\mathsf {NP}$$ -hard and challenging in p
Y. Low, J. Gonzalez, A. Kyrola, D. Bickson, C. Guestrin, and J. M. Hellerstein (2012).Distributed GraphLab: A Framework for Machine Learning and Data Mining in the CloudPVLDB J. Gonzalez, Y. Low, H. Gu, D. Bickson, and C. Guestrin (October, 2012).PowerGraph: Distributed Grap...
Extraction and Mining of Academic Social Networks. In Proceedings of the Fourteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD’2008). pp.990-998. [PDF(opens in new tab)] [Slides(opens in new tab)] [System(opens in new tab)] [API(opens in new tab...
The graph-based data mining for SV pattern searching includes four steps: deduplicate edges, generate the graph, subgraph mining, and reduce similar patterns. Deduplicate edges Since every cluster can include more than one breakpoint, it is likely to find clusters with more than one edge going ...
Recent years have witnessed a surge of interest in learning representations of graph-structured data, with applications from social networks to drug discovery. However, graph neural networks, the machine learning models for handling graph-structured data