Affiliation:
1. Lahore University of Management Sciences, Lahore, Pakistan
2. Islamic University, Madinah, Saudi Arabia, KSA
3. Hazara University, Mansehra, Pakistan
Abstract
In many graphs such as social networks, nodes have associated attributes representing their behavior. Predicting node attributes in such graphs is an important task with applications in many domains like recommendation systems, privacy preservation, and targeted advertisement. Attribute values can be predicted by treating each node as a data point described by attributes and employing classification/regression algorithms. However, in social networks, there is complex interdependence between node attributes and pairwise interaction. For instance, attributes of nodes are influenced by their neighbors (social influence), and neighborhoods (friendships) between nodes are established based on pairwise (dis)similarity between their attributes (social selection). In this article, we establish that information in network topology is extremely useful in determining node attributes. In particular, we use self- and cross-proclivity measures (quantitative measures of how much a node attribute depends on the same and other attributes of its neighbors) to predict node attributes. We propose a feature map to represent a node with respect to a specific attribute
a
, using all attributes of its
h
-hop neighbors. Different classifiers are then learned on these feature vectors to predict the value of attribute
a
. We perform extensive experimentation on 10 real-world datasets and show that the proposed method significantly outperforms known approaches in terms of prediction accuracy.
Publisher
Association for Computing Machinery (ACM)
Subject
Artificial Intelligence,Theoretical Computer Science
Cited by
34 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. MSDGSD: A Scalable Graph Descriptor for Processing Large Graphs;IEEE Transactions on Computational Social Systems;2024-06
2. Attribute subspace-guided multi-scale community detection;Neural Computing and Applications;2024-05-02
3. SsAG
: Summarization and Sparsification of Attributed Graphs;ACM Transactions on Knowledge Discovery from Data;2024-04-12
4. Node Classification with Multi-hop Graph Convolutional Network;Communications in Computer and Information Science;2024
5. Information We Can Extract About a User from ‘One Minute Mobile Application Usage’;IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS);2023-05-20