Eva: Attribute-Aware Network Segmentation

  title={Eva: Attribute-Aware Network Segmentation},
  author={Salvatore Citraro and Giulio Rossetti},
  booktitle={International Workshop on Complex Networks \& Their Applications},
Identifying topologically well-defined communities that are also homogeneous w.r.t. attributes carried by the nodes that compose them is a challenging social network analysis task. We address such a problem by introducing Eva, a bottom-up low complexity algorithm designed to identify network hidden mesoscale topologies by optimizing structural and attribute-homophilic clustering criteria. We evaluate the proposed approach on heterogeneous real-world labeled network datasets, such as co-citation… 

Identifying and exploiting homogeneous communities in labeled networks

This work addresses such a challenging task presenting Eva, an algorithmic approach designed to maximize a quality function tailoring both structural and homophilic clustering criteria, and suggests that Eva is the only method able to discover homogeneous clusters without considerably degrading partition modularity.

X-Mark: a benchmark for node-attributed community discovery algorithms

This work presents X-Mark, a model that generates synthetic node-attributed graphs with planted communities that consists in forming communities and node labels contextually while handling categorical or continuous attributive information.

X-Mark: a benchmark for node-attributed community discovery algorithms

This work presents X-Mark, a model that generates synthetic node-attributed graphs with planted communities, its novelty consists in forming communities and node labels contextually while handling categorical or continuous attributive information.

High Influencing Pattern Discovery over Time Series Data

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Klasterisasi Buku dan Peminjam Buku di Perpustakaan dengan Metode Analisis Jejaring Sosial dan Deteksi Komunitas

  • T. Setiadi
  • Computer Science
    INOVTEK Polbeng - Seri Informatika
  • 2022
The purpose of this study is to find book clusters and borrower clusters by utilizing the best community detection method obtained and the results of this clustering can be used as recommendations for library management in making library programs to increase the utility of books and increase user loyalty.

Toward a Standard Approach for Echo Chamber Detection: Reddit Case Study

A general framework to identify echo chambers on online social networks built on top of features they commonly share, based on a four-step pipeline that involves the identification of a controversial issue and the inference of users’ ideology on the controversy, is proposed.



Community Detection based on Structural and Attribute Similarities

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I-Louvain: An Attributed Graph Clustering Method

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Community detection in Attributed Network

A comparative study of some existing attributed network community detection algorithm on both synthetic data and on real world data is proposed.

Community Detection in Networks with Node Attributes

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Structure and attributes community detection: comparative analysis of composite, ensemble and selection methods

This paper compares the novel clustering method, termed Selection method, against seven clustering methods; it is shown that the Selection method out performed the state-of-art structure and attribute methods.

Fast unfolding of communities in large networks

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Learning to Discover Social Circles in Ego Networks

A novel machine learning task of identifying users' social circles is defined as a node clustering problem on a user's ego-network, a network of connections between her friends, and a model for detecting circles is developed that combines network structure as well as user profile information.

Graph Clustering Based on Structural/Attribute Similarities

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CDLIB: a python library to extract, compare and evaluate communities from complex networks

The aim of CDlib is to allow easy and standardized access to a wide variety of network clustering algorithms, to evaluate and compare the results they provide, and to visualize them.

Near linear time algorithm to detect community structures in large-scale networks.

This paper investigates a simple label propagation algorithm that uses the network structure alone as its guide and requires neither optimization of a predefined objective function nor prior information about the communities.