3.9 Article

Learning attribute and homophily measures through random walks

Related references

Note: Only part of the references are listed.
Article Computer Science, Information Systems

Robust graph regularization nonnegative matrix factorization for link prediction in attributed networks

Elahe Nasiri et al.

Summary: Link prediction is a widely studied problem in complex network analysis. The existing methods often overlook the potential of nodal attributes and only focus on the network's topological structure. To address this limitation, a novel method called RGNMF-AN was proposed, which models both the topological structure and nodal attributes for link prediction. The method combines network topology and nodal attribute information and calculates high-order proximities using the SARWS method. Empirical findings on real-world complex network datasets show that the combination of attributed and topological information significantly improves prediction performance compared to baseline and other NMF-based algorithms.

MULTIMEDIA TOOLS AND APPLICATIONS (2023)

Article Engineering, Multidisciplinary

Graph Regularized Nonnegative Matrix Factorization for Community Detection in Attributed Networks

Kamal Berahmand et al.

Summary: Community detection is an important research topic in machine learning, but most existing methods only consider the network's topology structure, neglecting the advantage of using node attribute information. To solve this problem, we propose a novel Augment Graph Regularization Nonnegative Matrix Factorization method, which performs unexpectedly well in attributed networks.

IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING (2023)

Article Multidisciplinary Sciences

The dynamic nature of percolation on networks with triadic interactions

Hanlin Sun et al.

Summary: The authors study percolation in networks with higher-order interactions and propose a triadic percolation model. They find that the connectivity of the network changes in time and that the order parameter undergoes period doubling and chaos. They develop a theory for triadic percolation on random graphs and find similar phenomena in real network topologies. These findings change our understanding of percolation and have important implications for studying dynamic and complex systems such as neural and climate networks.

NATURE COMMUNICATIONS (2023)

Proceedings Paper Computer Science, Interdisciplinary Applications

Learning Attribute Distributions Through RandomWalks

Nelson Antunes et al.

Summary: This paper investigates the statistical learning of nodal attribute distributions in homophily networks using random walks. The study includes both discrete and continuous attributes. Various existing canonical models, based on preferential attachment, are generalized. The performance of attribute agnostic sampling schemes is studied on synthetic networks and real world systems, considering the degree of homophily.

COMPLEX NETWORKS AND THEIR APPLICATIONS XI, COMPLEX NETWORKS 2022, VOL 2 (2023)

Article Multidisciplinary Sciences

Inequality and inequity in network-based ranking and recommendation algorithms

Lisette Espin-Noboa et al.

Summary: This study examines how PageRank and Who-to-Follow algorithms can generate inequality and inequity when applied to directed social networks, proposing a directed network model with preferential attachment and homophily. The findings suggest that inequality is positively correlated with inequity, driven by network structure factors, while inequity is influenced by homophily and minority size. The algorithms reduce, replicate, and amplify the representation of minorities in rankings, with minorities potentially improving visibility by strategically connecting in the network.

SCIENTIFIC REPORTS (2022)

Article Multidisciplinary Sciences

Epidemics on multilayer simplicial complexes

Junfeng Fan et al.

Summary: Simplicial complexes describe the phenomenon that in social networks, a link can connect more than two individuals. This has significant implications for epidemic spreading, particularly in a multilayer network model. The social network layer transmits information through interactions among individuals, while the physical layer is responsible for epidemic transmission. We applied a microscopic Markov chain approach to study probability transition equations and epidemic outbreak thresholds, and validated the results with Monte Carlo simulations. We found that when disease transmission rates were low or medium in the physical network, information transmission rates were frequently low as well; however, introducing 2-simplex interactions in the social network effectively mitigated this issue.

PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES (2022)

Review Multidisciplinary Sciences

Dynamics on higher-order networks: a review

Soumen Majhi et al.

Summary: Higher-order networks, which allow links to connect more than two nodes, have emerged as a new frontier in network science and have led to important discoveries in various fields. This review focuses on the dynamics that arise on higher-order networks, covering different processes such as synchronization, contagion, cooperation, and consensus formation. The review also outlines future challenges and promising research directions.

JOURNAL OF THE ROYAL SOCIETY INTERFACE (2022)

Article Computer Science, Artificial Intelligence

Attributed network representation learning via improved graph attention with robust negative sampling

Huilian Fan et al.

Summary: The study introduces a novel graph auto-encoder method to capture structural features and node attributes of attributed networks, optimizing embedded vectors through negative sampling and weighted neighborhood attributes, suggesting an algorithm to balance the reconstruction loss of node attributes and structural features.

APPLIED INTELLIGENCE (2021)

Article Statistics & Probability

Sampling Based Estimation of In-Degree Distribution for Directed Complex Networks

Nelson Antunes et al.

Summary: This work focuses on estimating the in-degree distribution of directed networks from sampling network nodes or edges. Two estimation approaches are proposed, based on inversion and asymptotic methods. The performance of these approaches is tested on synthetic and real networks, showing good results.

JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS (2021)

Article Computer Science, Information Systems

Attribute-Guided Network Sampling Mechanisms

Suhansanu Kumar et al.

Summary: This article introduces a novel task-independent sampler for attributed networks that greedily adds the most informative node to the sample, rapidly exploring the attribute space. Content sampling is proven to be an NP-hard problem, but surprise-based samplers are shown to be sample efficient and outperform attribute-agnostic samplers by a wide margin in real-world datasets.

ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA (2021)

Article Computer Science, Theory & Methods

Explaining classification performance and bias via network structure and sampling technique

Lisette Espin-Noboa et al.

Summary: Social networks serve as important carriers of information, with friends' political leanings acting as proxies to identify one's own preferences. Structural properties of the network and training sample influence collective classification results, with findings helping practitioners better understand and evaluate results when sampling budgets are limited or ground-truth is unavailable.

APPLIED NETWORK SCIENCE (2021)

Article Social Sciences, Mathematical Methods

Sampling methods and estimation of triangle count distributions in large networks

Nelson Antunes et al.

Summary: This paper investigates the distributions of triangle counts per vertex and edge through sampling, especially for large networks. A novel sampling method tailored for the estimation analysis is proposed, with consideration for multiple network access scenarios. The estimation methods are evaluated on synthetic and real-world networks in a data study.

NETWORK SCIENCE (2021)

Review Physics, Multidisciplinary

Networks beyond pairwise interactions: Structure and dynamics

Federico Battiston et al.

PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS (2020)

Article Multidisciplinary Sciences

Simplicial models of social contagion

Iacopo Iacopini et al.

NATURE COMMUNICATIONS (2019)

Article Engineering, Multidisciplinary

Exponentially Twisted Sampling for Centrality Analysis and Community Detection in Attributed Networks

Cheng-Hsun Chang et al.

IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING (2019)

Article Multidisciplinary Sciences

Homophily influences ranking of minorities in social networks

Fariba Karimi et al.

SCIENTIFIC REPORTS (2018)

Article Computer Science, Software Engineering

Generalized PageRank on Directed Configuration Networks

Ningyuan Chen et al.

RANDOM STRUCTURES & ALGORITHMS (2017)

Article Physics, Fluids & Plasmas

Weighted growing simplicial complexes

Owen T. Courtney et al.

PHYSICAL REVIEW E (2017)

Article Statistics & Probability

ASYMPTOTIC BEHAVIOR AND DISTRIBUTIONAL LIMITS OF PREFERENTIAL ATTACHMENT GRAPHS

Noam Berger et al.

ANNALS OF PROBABILITY (2014)

Article Statistics & Probability

Geometric preferential attachment in non-uniform metric spaces

Jonathan Jordan

ELECTRONIC JOURNAL OF PROBABILITY (2013)

Article Physics, Condensed Matter

Scale-free homophilic network

Mauricio L. de Almeida et al.

EUROPEAN PHYSICAL JOURNAL B (2013)

Article Multidisciplinary Sciences

Distribution of node characteristics in complex networks

Juyong Park et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2007)

Article Mathematics, Applied

Non-backtracking random walks mix faster

Noga Alon et al.

COMMUNICATIONS IN CONTEMPORARY MATHEMATICS (2007)

Article Physics, Multidisciplinary

Bose-Einstein condensation in complex networks

G Bianconi et al.

PHYSICAL REVIEW LETTERS (2001)

Article Physics, Fluids & Plasmas

Organization of growing random networks

PL Krapivsky et al.

PHYSICAL REVIEW E (2001)