Special Session on
“Computational Intelligence for Social Network Analysis”
Aim and Scope
Nowadays online social networks have become an essential part of our life. Online social networks are also being utilized as a platform for marketing and promoting goods, political campaigns, rumour control, recruitment population screening, epidemiology, trend analysis, and many more. To utilize online social networks, we need to understand the qualitative and quantitative relationship among the entities of the networks. This qualitative and quantitative analysis process is referred to as Social Network Analysis (SNA). In general, the analysis process first aims at determining the appropriate (or, say, influential) node in the networks and then aims to determine the node's influence in the network. In the last two decades, evolutionary algorithms have emerged as the most suited tool for selecting a candidate from a region of unknown nature. Evolutionary algorithms are machine learning methods that mimic the process of natural selection to optimize a solution to a problem. SNA and evolutionary algorithms can be used together to improve the accuracy and efficiency of social media data analysis. Evolutionary algorithms are machine learning techniques that mimic the process of natural selection to optimize a solution to a problem. They can be used to optimize various aspects of the social network, such as:
- Sentiment analysis : Evolutionary algorithms can be used to optimize the parameters of sentiment analysis models, such as the weighting of different features or the selection of specific words or phrases that indicate sentiment.
- Opinion mining : Evolutionary algorithms can optimize the parameters of opinion mining models, such as the weighting of different features or the selection of specific words or phrases that indicate opinion.
- Brand monitoring : Evolutionary algorithms can be used to optimize the parameters of brand monitoring models, such as the weighting of different features or the selection of specific keywords or hashtags to track.
- Influencer identification : Evolutionary algorithms can be used to optimize the parameters of influencer identification models, such as the weighting of different features or the selection of specific criteria for identifying influential individuals.
- Trend analysis : Evolutionary algorithms can optimize the parameters of trend analysis models, such as the weighting of different features or the selection of specific keywords or hashtags to track over time.
This session aims to discuss evolutionary approaches for extracting meaningful information to optimize the outcome of social network analysis. Topics of interest include but are not limited to:
- Node classification
- Community detection
- Link prediction problems
- Influence maximization
- Misbehaviour detection in communities
- Contextual social network analysis
- Temporal analysis on social networks topologies
- Multi-agent based social network modelling and analysis
- Collective social network integration and analysis and
- Any other related topics
Paper Submission
Potential authors may submit their manuscripts for presentation consideration through ICETCI 2023 submission system electronically at https://edas.info/N30369 , following the conference guidelines. All submissions will go through peer review process. To submit your paper to this special session, you have to choose our special session title on the submission page.
Important Dates
| Paper submission deadline: | Apr 15, 2023 |
| Paper acceptance notification date: | June 08, 2023 |
| Final paper submission and early registration deadline: | June 25, 2023 |
Organizers
Prof. Pramod Kumar Singh and Dr. Avadh Kishor
Department of Computer Science and Engineering, ABV - Indian Institute of Information Technology and Management Gwalior, MP, India
E-mail: pksingh@iiitm.ac.in , akishor@iiitm.ac.in
Bio: P K Singh: https://www.researchgate.net/profile/Pramod-Singh-8
A Kishor: https://sites.google.com/iiitm.ac.in/abv-iiitm-gwalior/faculty?authuser=0
Dr. Bharadwaj Veeravalli
Department of Electrical and Computer Engineering, National University of Singapore (NUS) Singapore
E-mail: elebv@nus.edu.sg
Bio: https://www.ece.nus.edu.sg/stfpage/elebv/