Fast Algorithms for Intimate-Core Group Search in Weighted Graphs

Retrieving Top Weighted Triangles in Graphs

Pattern counting in graphs is a fundamental primitive for many network analysis tasks, and a number of methods have been developed for scaling subgraph counting to large graphs. Many real-world networks carry a natural notion of strength of connection between nodes, which are often modeled by a weighted graph, but existing scalable graph algorithms for pattern mining are designed for unweighted graphs. Here, we develop a suite of deterministic and random sampling algorithms that enable the fast discovery of the 3-cliques (triangles) with the largest weight in a graph, where weight is measured …

A GPU-Based Solution to Fast Calculation of Betweenness Centrality on Large Weighted Networks

Recent decades have witnessed the tremendous development of network science, which indeed brings a new and insightful language to model real systems of different domains. Betweenness, a widely employed centrality in network science, is a decent proxy in investigating network loads and rankings. However, the extremely high computational cost greatly prevents its applying on large networks. Though several parallel algorithms have been presented to reduce its calculation cost on unweighted networks, a fast solution for weighted networks, which are in fact more ubiquitous than unweighted ones in r…

加权时序网络中的快速极大团枚举

Fast maximal clique enumeration in weighted temporal networks

本文提出高效的加权时序网络 (delta,gamma)-极大团枚举算法，改进两阶段流程并实现线性初始阶段与搜索剪枝，在真实数据集上较现有方法取得数个数量级加速。

用于加权图稀疏化的网络主干快速非参数推断

Fast nonparametric inference of network backbones for weighted graph sparsification

针对现有加权网络主干提取方法需设自由参数或受限拓扑的问题，提出基于MDL准则的非参数框架，含全局/局部目标函数与对数线性复杂度的最优贪心算法，在保网络特性的同时实现高效稀疏化。

Fast filtering and animation of large dynamic networks

Detecting and visualizing what are the most relevant changes in an evolving network is an open challenge in several domains. We present a fast algorithm that filters subsets of the strongest nodes and edges representing an evolving weighted graph and visualize it by either creating a movie, or by streaming it to an interactive network visualization tool. The algorithm is an approximation of exponential sliding time-window that scales linearly with the number of interactions. We compare the algorithm against rectangular and exponential sliding time-window methods. Our network filtering algorith…

Fast Response to Infection Spread and Cyber Attacks on Large-Scale Networks

We present a strategy for designing fast methods of response to cyber attacks and infection spread on complex weighted networks. In these networks, nodes can be interpreted as primitive elements of the system, and weighted edges reflect the strength of interaction among these elements. The proposed strategy belongs to the family of multiscale methods whose goal is to approximate the system at multiple scales of coarseness and to obtain a solution of microscopic scale by combining the information from coarse scales. In recent years these methods have demonstrated their potential for solving opt…