具有质量保证的局部中心性最小化

基于邻域优化的有效边中心性

Effective Edge Centrality via Neighborhood-based Optimization

本文提出基于邻域优化的边中心性度量ECHO，并给出线性复杂度估计算法及理论精度保证，在真实网络实验中相比六种现有方法具有更优效果和更高效率。

图的一种 $α$-三角形特征向量中心性

An $α$-triangle eigenvector centrality of graphs

本文提出融合边与三角形结构的 $α$TEC 全局中心性，利用非负张量谱半径特征向量评分，并通过参数 $α$ 调节两类结构贡献，实验验证其能有效识别重要顶点。

吸收随机游走中心性：理论与算法

Absorbing random-walk centrality: Theory and algorithms

研究了基于吸收随机游动的图中心性的新概念。给定一个图$G=(V,E)$和一组查询节点$Q\subseteq V$，我们的目标是确定$k$最中心的节点在$G$相对于$Q$。具体来说，我们认为中心节点是吸收的随机游走开始于查询节点$Q$。我们的目标是找到一组$k$中心节点，使随机游走的预期长度最小化，直到吸收。所提出的措施，我们称之为$k$吸收随机游走中心，有利于不同的集，因为它是有益的，以放置$k$吸收节点在图的不同部分，以便“拦截”随机游走，从不同的查询节点开始。尽管在文献中已经考虑了类似的问题定义，在信息检索设置的目标是多样化的Web搜索结果，在本文中，我们正式研究的问题，并证明了它的一些属性。我们表明，该问题是NP-难的，而目标函数是单调的和超模，这意味着一个贪心算法提供的解决方案与近似保证。另一方面，贪心算法涉及昂贵的矩阵运算，这使得在大型数据集上使用它是不可行的。为了应对这一挑战，我们开发了更有效的算法，基于谱聚类和个性化的PageRank。

Blind Inference of Eigenvector Centrality Rankings

We consider the problem of estimating a network's eigenvector centrality only from data on the nodes, with no information about network topology. Leveraging the versatility of graph filters to model network processes, data supported on the nodes is modeled as a graph signal obtained via the output of a graph filter applied to white noise. We seek to simplify the downstream task of centrality ranking by bypassing network topology inference methods and, instead, inferring the centrality structure of the graph directly from the graph signals. To this end, we propose two simple algorithms for rank…

Efficient Approximation Algorithms for Adaptive Seed Minimization

As a dual problem of influence maximization, the seed minimization problem asks for the minimum number of seed nodes to influence a required number $η$ of users in a given social network $G$. Existing algorithms for seed minimization mostly consider the non-adaptive setting, where all seed nodes are selected in one batch without observing how they may influence other users. In this paper, we study seed minimization in the adaptive setting, where the seed nodes are selected in several batches, such that the choice of a batch may exploit information about the actual influence of the previous bat…

Differentially-Private Two-Party Egocentric Betweenness Centrality

We describe a novel protocol for computing the egocentric betweenness centrality of a node when relevant edge information is spread between two mutually distrusting parties such as two telecommunications providers. While each node belongs to one network or the other, its ego network might include edges unknown to its network provider. We develop a protocol of differentially-private mechanisms to hide each network's internal edge structure from the other; and contribute a new two-stage stratified sampler for exponential improvement to time and space efficiency. Empirical results on several open…

MANTRA：通过采样实现时间介数中心性近似

MANTRA: Temporal Betweenness Centrality Approximation through Sampling

本文提出 MANTRA 框架，结合渐进式 Monte Carlo Empirical Rademacher Averages 采样近似时间图节点与时间边的介数中心性，并以理论复杂度和真实网络实验验证其效率与精度。

Node and layer eigenvector centralities for multiplex networks

Eigenvector-based centrality measures are among the most popular centrality measures in network science. The underlying idea is intuitive and the mathematical description is extremely simple in the framework of standard, mono-layer networks. Moreover, several efficient computational tools are available for their computation. Moving up in dimensionality, several efforts have been made in the past to describe an eigenvector-based centrality measure that generalizes Bonacich index to the case of multiplex networks. In this work, we propose a new definition of eigenvector centrality that relies on…

Targeting interventions for displacement minimization in opinion dynamics

Social influence is largely recognized as a key factor in opinion formation processes. Recently, the role of external forces in inducing opinion displacement and polarization in social networks has attracted significant attention. This is in particular motivated by the necessity to understand and possibly prevent interference phenomena during political campaigns and elections. In this paper, we formulate and solve a targeted intervention problem for opinion displacement minimization on a social network. Specifically, we consider a min-max problem whereby a social planner (the defender) aims at…

复杂传染动态中的过去感知博弈中心性

Past-aware game-theoretic centrality: a framework for cardinality-constrained set-function maximization on networks

本文提出了一种考虑过去合作的博弈中心性方法，用于复杂传染动态中的影响最大化问题，通过高效算法提升节点影响力识别效率和质量。

Optimal Intervention on Weighted Networks via Edge Centrality

Suppose there is a spreading process such as an infectious disease propagating on a graph. How would we reduce the number of affected nodes in the spreading process? This question appears in recent studies about implementing mobility interventions on mobility networks (Chang et al. (2021)). A practical algorithm to reduce infections on unweighted graphs is to remove edges with the highest edge centrality score (Tong et al. (2012)), which is the product of two adjacent nodes' eigenscores. However, mobility networks have weighted edges; Thus, an intervention measure would involve edge-weight red…

在不确定性的中间节点：一种吸收马尔可夫链方法

Computing Absorbing-Frequency Centrality: Complexity, Estimators, and Scalable Algorithms for Stochastic Networks

本文提出一种针对随机网络的介数中心性度量方法，通过吸收马尔可夫链模型研究不确定环境下的节点重要性，并通过蒙特卡洛模拟进行估计。

SaPHyRa: A Learning Theory Approach to Ranking Nodes in Large Networks

Ranking nodes based on their centrality stands a fundamental, yet, challenging problem in large-scale networks. Approximate methods can quickly estimate nodes' centrality and identify the most central nodes, but the ranking for the majority of remaining nodes may be meaningless. For example, ranking for less-known websites in search queries is known to be noisy and unstable. To this end, we investigate a new node ranking problem with two important distinctions: a) ranking quality, rather than the centrality estimation quality, as the primary objective; and b) ranking only nodes of interest, e.…

多维、多层、非线性和动态HITS

Multi-Dimensional, Multilayer, Nonlinear and Dynamic HITS

本文提出了一种基于多齐次顺序保持映射的 Perron 特征向量的时序多维加权有向网络排名模型，扩展了HITS算法到时序多层设置，并定义了五个中心性向量，包括节点、层和时间戳的向量，通过非线性引入保证了任何网络的中心性向量存在性和唯一性。

利用复杂网络着色识别有效多传播者

Identifying effective multiple spreaders by coloring complex networks

如何识别社交网络中的影响节点具有重要的理论意义，它关系到如何防止传染病传播或连锁故障，如何加速信息的扩散等问题，本文将图论中的着色问题的思想推广到复杂网络中，尝试寻找复杂网络中的\emph{effective multiple spreaders}.在我们的方法中，网络中的每个节点都用一种颜色着色，具有相同颜色的节点被排序到一个独立的集合中。然后，对于一个给定的中心性指数，在一个独立的集合中具有最高中心性的节点被选为多个传播器。与传统的从\emph{entire}网络角度选择中心度最高的节点的方法相比，无论是在网络模型中还是在真实的社交网络中，本文的方法都能更有效地加速传播过程，最大化传播覆盖率.同时，着色算法的低计算复杂度保证了我们的方法的潜在应用。