Networks (or interchangeably graphs) have been ubiquitous across the globe and within science and engineering: social networks, collaboration networks, protein-protein interaction networks, infrastructure networks, among many others. Machine learning on graphs, especially network representation learning, has shown remarkable performance in tasks related to graphs, such as node/graph classification, graph clustering, and link prediction. These tasks are closely related to the Web applications, especially social network analysis and recommendation systems. For example, node classification and graph clustering are widely used for studies on community detection, and link prediction plays a vital role in friend or item recommendation. Like performance, it is equally crucial for individuals to understand the behavior of machine learning models and be able to explain how these models arrive at a certain decision. Such needs have motivated many studies on interpretability in machine learning. Specifically, for social network analysis, we may need to know the reasons why certain users (or groups) are classified or clustered together by the machine learning models, or why a friend recommendation system considers some users similar so that they are recommended to connect with each other. Under such circumstances, an interpretable network representation is necessary and it should carry the graph information to a level understandable by humans. In this tutorial, we will (1) define interpretability and go over its definitions within different contexts in studies of networks; (2) review and summarize various interpretable network representations; (3) discuss connections to network embedding, graph summarization, and network visualization methods; (4) discuss explainability in Graph Neural Networks, as such techniques are often perceived to have limited interpretability; and (5) highlight the open research problems and future research directions. The tutorial is designed for researchers, graduate students, and practitioners in areas such as graph mining, machine learning on graphs, and machine learning interpretability. Few prerequisites are required for The Web Conferenc participants to attend.
@inproceedings{shengmin2022interpretable,
title = {Interpretable Network Representations},
author = {Shengmin Jin and Danai Koutra and Reza Zafarani},
year = {2022},
keywords = {tutorial},
booktitle = {Companion Proceedings of The Web Conference 2022 (WWW)},
abstract = {Networks (or interchangeably graphs) have been ubiquitous across the globe and within science and engineering: social networks, collaboration networks, protein-protein interaction networks, infrastructure networks, among many others. Machine learning on graphs, especially network representation learning, has shown remarkable performance in tasks related to graphs, such as node/graph classification, graph clustering, and link prediction. These tasks are closely related to the Web applications, especially social network analysis and recommendation systems. For example, node classification and graph clustering are widely used for studies on community detection, and link prediction plays a vital role in friend or item recommendation. Like performance, it is equally crucial for individuals to understand the behavior of machine learning models and be able to explain how these models arrive at a certain decision. Such needs have motivated many studies on interpretability in machine learning. Specifically, for social network analysis, we may need to know the reasons why certain users (or groups) are classified or clustered together by the machine learning models, or why a friend recommendation system considers some users similar so that they are recommended to connect with each other. Under such circumstances, an interpretable network representation is necessary and it should carry the graph information to a level understandable by humans. In this tutorial, we will (1) define interpretability and go over its definitions within different contexts in studies of networks; (2) review and summarize various interpretable network representations; (3) discuss connections to network embedding, graph summarization, and network visualization methods; (4) discuss explainability in Graph Neural Networks, as such techniques are often perceived to have limited interpretability; and (5) highlight the open research problems and future research directions. The tutorial is designed for researchers, graduate students, and practitioners in areas such as graph mining, machine learning on graphs, and machine learning interpretability. Few prerequisites are required for The Web Conferenc participants to attend.},
url = {https://shengminjin.github.io/tutorials/www2022},
}