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Machine learning with graphs

Machine learning with graphs, Shobeir Fakhraei






Data that are best represented as a graph such as social, biological, communication, or transportation networks, and energy grids are ubiquitous in our world today. As more of such structured and semi-structured data is becoming available, the machine learning methods that can leverage the signal in these data are becoming more valuable, and the importance of being able to effectively mine and learn from such data is growing.

These graphs are typically multi-relational, dynamic, and large-scale. Understanding the different techniques applicable to graph data, dealing with their heterogeneity and applications of methods for information integration and alignment, handling dynamic and changing graphs, and addressing each of these issues at scale are some of the challenges in developing machine learning methods for graph data that appear in a variety of applications.

In this special issue, we aim to publish articles that help us better understand the principles, limitations, and applications of current graph-based machine learning methods, and to inspire research on new algorithms, techniques, and domain analysis for machine learning with graphs.

Lead guest editor
Shobeir Fakhraei, Information Science Institute, Univ. of Southern California
Guest editors
Austin Benson, Computer Science Department, Cornell University
Ciro Cattuto, ISI Foundation
Danai Koutra, Computer Science & Engineering, University of Michigan
Vagelis Papalexakis, Computer Science & Engineering, UC Riverside
Jiliang Tang, Computer Science & Engineering Dept., Michigan State University


  1. Important data mining problems such as nearest-neighbor search and clustering admit theoretical guarantees when restricted to objects embedded in a metric space. Graphs are ubiquitous, and clustering and class...

    Authors: José Bento and Stratis Ioannidis

    Citation: Applied Network Science 2019 4:107

    Content type: Research

    Published on:

  2. The goal of this study is to demonstrate how network science and graph theory tools and concepts can be effectively used for exploring and comparing semantic spaces of word embeddings and lexical databases. Sp...

    Authors: Alexander Veremyev, Alexander Semenov, Eduardo L. Pasiliao and Vladimir Boginski

    Citation: Applied Network Science 2019 4:104

    Content type: Research

    Published on:

  3. The success of graph embeddings or nodrepresentation learning in a variety of downstream tasks, such as node classification, link prediction, and recommendation systems, has led to their popularity in recent y...

    Authors: Saba A. Al-Sayouri, Danai Koutra, Evangelos E. Papalexakis and Sarah S. Lam

    Citation: Applied Network Science 2019 4:88

    Content type: Research

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  4. A main challenge in mining network-based data is finding effective ways to represent or encode graph structures so that it can be efficiently exploited by machine learning algorithms. Several methods have focu...

    Authors: Leonardo Gutiérrez-Gómez and Jean-Charles Delvenne

    Citation: Applied Network Science 2019 4:82

    Content type: Research

    Published on:

  5. Similarity measures are used extensively in machine learning and data science algorithms. The newly proposed graph Relative Hausdorff (RH) distance is a lightweight yet nuanced similarity measure for quantifyi...

    Authors: Sinan G. Aksoy, Kathleen E. Nowak, Emilie Purvine and Stephen J. Young

    Citation: Applied Network Science 2019 4:80

    Content type: Research

    Published on:

  6. Information networks are becoming increasingly popular to capture complex relationships across various disciplines, such as social networks, citation networks, and biological networks. The primary challenge in...

    Authors: Mehmet E. Aktas, Esra Akbas and Ahmed El Fatmaoui

    Citation: Applied Network Science 2019 4:61

    Content type: Review

    Published on:

  7. PageRank for Semi-Supervised Learning has shown to leverage data structures and limited tagged examples to yield meaningful classification. Despite successes, classification performance can still be improved, ...

    Authors: Esteban Bautista, Patrice Abry and Paulo Gonçalves

    Citation: Applied Network Science 2019 4:57

    Content type: Research

    Published on: