scholarly journals Label-Dependent Feature Extraction in Social Networks for Node Classification

2010 ◽  
pp. 89-102 ◽  
Author(s):  
Tomasz Kajdanowicz ◽  
Przemysław Kazienko ◽  
Piotr Doskocz
Keyword(s):  
Social Networks ◽  
Feature Extraction ◽  
Node Classification

scholarly journals Cooperative-routing mechanism based on node classification and task allocation for opportunistic social networks

2020 ◽  
Vol 14 (3) ◽  
pp. 420-429 ◽  
Author(s):  
Wenyu Zheng ◽  
Zhigang Chen ◽  
Jia Wu ◽  
Kanghuai Liu
Keyword(s):  
Social Networks ◽  
Task Allocation ◽  
Cooperative Routing ◽  
Routing Mechanism ◽  
Node Classification ◽  
And Task

An on-line sequential learning method in social networks for node classification

2015 ◽  
Vol 149 ◽  
pp. 207-214 ◽  
Author(s):  
Yongjiao Sun ◽  
Ye Yuan ◽  
Guoren Wang
Keyword(s):  
Social Networks ◽  
Sequential Learning ◽  
Learning Method ◽  
On Line ◽  
Node Classification

scholarly journals Improving distributed vector representation of short and noisy texts in the context of online classification

2019 ◽  
Author(s):  
Renato Silva ◽  
Johannes Lochter ◽  
Tiago Almeida
Keyword(s):  
Social Networks ◽  
Feature Extraction ◽  
Data Augmentation ◽  
Text Messages ◽  
Vector Representation ◽  
Online Classification ◽  
Textual Representation ◽  
Noisy Text

The classification of messages generated by users on social networks and other Internet platforms is challenging because they are often short and full of slang, abbreviations, and idioms, which hinders the feature extraction. To address this problem, this study proposes a data augmentation technique to increase the number of data in order to improve the quality of the textual representation model and, consequently, the performance in the classification. The proposed technique is evaluated in the online classification of sentiments in Twitter messages. The experiments were carefully performed and a statistical analysis of the results indicated that the data augmentation is effective in online classification of short and noisy text messages.

scholarly journals Social behavior prediction with graph U-Net+

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Zhiyue Yan ◽  
Wenming Cao ◽  
Jianhua Ji
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Feature Extraction ◽  
Binary Classification ◽  
Representation Learning ◽  
Graph Representation ◽  
Behavior Prediction ◽  
Graph Node ◽  
Future Behavior ◽  
Decoder Architecture

AbstractWe focus on the problem of predicting social media user’s future behavior and consider it as a graph node binary classification task. Existing works use graph representation learning methods to give each node an embedding vector, then update the node representations by designing different information passing and aggregation mechanisms, like GCN or GAT methods. In this paper, we follow the fact that social media users have influence on their neighbor area, and extract subgraph structures from real-world social networks. We propose an encoder–decoder architecture based on graph U-Net, known as the graph U-Net+. In order to improve the feature extraction capability in convolutional process and eliminate the effect of over-smoothing problem, we introduce the bilinear information aggregator and NodeNorm normalization approaches into both encoding and decoding blocks. We reuse four datasets from DeepInf and extensive experimental results demonstrate that our methods achieve better performance than previous models.

Analytical framework for mental health feature extraction methods in social networks

2021 ◽  
pp. 1-14
Author(s):  
Nazila Taghvaei ◽  
Behrooz Masoumi ◽  
Mohammad Reza Keyvanpour
Keyword(s):  
Mental Health ◽  
Social Networks ◽  
Feature Extraction ◽  
Social Network ◽  
Mental Disorders ◽  
Social Relations ◽  
Mental States ◽  
Extraction Methods ◽  
Virtual Space ◽  
Internet Technology

Today, with the development of internet technology, a new kind of social relations and interactions have been formed in the newly emerged social networks. Through social networks, the users can share different types of content, including personal information, text, image, video, music, poem, and other related information, which express their mental states, emotions, feelings, and thoughts. Thus, a new and essential aspect of human life is being formed in a virtual space in social networks, which must be explored from several viewpoints, such as mental disorders. Analyzing mental disorders according to the social network data can guide us to gain new approaches to improve the public health of the whole society. To this aim, developing mental health feature extraction (MHFE) methods in a social network is essential and is now becoming an active research area. Therefore, in this paper, a review of existing techniques and methods in MHFE is presented, and a comprehensive framework is provided to classify these approaches. Furthermore, to analyze and evaluate each approach in extraction methods, an appropriate set of functional criteria is proposed, which leads to a more accurate understanding and correct use of them.

A meta-heuristic framework based on clustering and preprocessed datasets for solving the link prediction problem

2018 ◽  
Vol 45 (6) ◽  
pp. 794-817 ◽  
Author(s):  
Reham Shawqi Barham ◽  
Ahmad Sharieh ◽  
Azzam Sleit
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Social Networks ◽  
Feature Extraction ◽  
Feature Selection ◽  
Heuristic Algorithm ◽  
Time Complexity ◽  
Link Prediction ◽  
Prediction Problem ◽  
Under Sampling

This study presents a solution to a problem commonly known as link prediction problem. Link prediction problem interests in predicting the possibility of appearing a connection between two nodes of a network, while there is no connection between these nodes in the present state of the network. Finding a solution to link prediction problem attracts variety of computer science fields such as data mining and machine learning. This attraction is due to its importance for many applications such as social networks, bioinformatics and co-authorship networks. Towards solving this problem, Evolutionary Link Prediction (EVO-LP) framework is proposed, presented, analysed and tested. EVO-LP is a framework that includes dataset preprocessing approach and a meta-heuristic algorithm based on clustering for prediction. EVO-LP is divided into preprocessing stage and link prediction stage. Feature extraction, data under-sampling and feature selection are utilised in the preprocessing stage, while in the prediction stage, a meta-heuristic algorithm based on clustering is used as an optimiser. Experimental results on a number of real networks show that EVO-LP improves the prediction quality with low time complexity.

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