Data Mining and Machine Learning in Social Network Analysis

Dear Colleagues,

Data mining and machine learning have found significant applications in the realm of social networks, transforming the way we understand and interact with online communities. These technologies enable the extraction of valuable insights from the massive volumes of data generated on platforms like Facebook, Twitter, and Instagram. By analyzing user behavior, content interactions, and network structures, data mining uncovers hidden patterns and trends that inform personalized content recommendations, targeted advertising, and even sentiment analysis. Machine learning algorithms, on the other hand, play a pivotal role in predicting user preferences, identifying influencers, and detecting anomalies such as fake accounts or cyberbullying.

In the context of social networks, the synergy of data mining and machine learning drives the development of recommendation systems that cater to individual interests, enhancing user engagement and retention. Moreover, the integration of these technologies allows platforms to combat the spread of misinformation and harmful content by recognizing patterns of virality and identifying sources of fake news. As social networks continue to evolve, data mining and machine learning promise to reshape user experiences, fostering more personalized, secure, and socially responsible interactions in the digital landscape. However, ethical considerations surrounding data privacy, algorithmic biases, and potential misuse highlight the need for a thoughtful and balanced approach in leveraging these technologies for the benefit of both users and society as a whole.

This Special Issue will accept publications that fall within the following research topics:

• Development of novel machine learning algorithms to identify and classify communities within social networks based on structural and behavioral patterns.
• Influence propagation modeling: investigating machine learning approaches to model and predict the spread of influence and information within social networks.
• Anomaly detection: design of techniques using machine learning to detect anomalous behaviors and activities within social networks, such as bots, spam, and unusual user interactions.
• Link prediction: exploring predictive models using machine learning to forecast future connections between users or entities in social networks.
• Sentiment analysis: development of advanced sentiment analysis methods using machine learning to understand and predict user emotions and opinions within social media posts.
• User profiling and personalization: utilizing machine learning to create accurate user profiles for personalized content recommendation and targeted advertising in social networks.
• Fake news detection: designing machine learning algorithms to identify and combat the dissemination of fake news and misinformation within social networks.
• Opinion dynamics modeling: investigating how machine learning can be employed to model the evolution of opinions and beliefs in social networks over time.
• Network evolution prediction: development of predictive models using machine learning to anticipate changes and shifts in the structure and dynamics of social networks.
• Graph representation learning: exploring techniques for learning informative node and graph embeddings in social networks, enhancing various downstream tasks.
• Network robustness analysis: using machine learning to study the vulnerability and resilience of social networks against attacks, failures, and cascading events.
• Privacy preservation: researching machine learning methods to analyze and mitigate privacy risks in social networks while preserving data utility.
• Temporal network analysis: development of models using machine learning to analyze the temporal dynamics of social networks and capture patterns of interactions over time.
• Behavioral pattern recognition: designing algorithms that utilize machine learning to recognize recurring behavioral patterns and trends within social network activities.
• Cross-network analysis: investigating methods to combine information from multiple social networks or platforms using machine learning to gain deeper insights.
• Network visualization: exploring machine learning-driven visualization techniques to represent complex social network structures and interactions in interpretable ways.
• Opinion leaders’ identification: developing machine learning approaches to identify influential users and opinion leaders within social networks based on their impact and interactions.
• Gender and demographic analysis: using machine learning to infer user gender, age, and other demographics from their social network activities, enabling targeted studies.
• Network fairness and bias: researching machine learning techniques to identify and mitigate biases in social network algorithms that can lead to unfair outcomes.
• Multi-modal social network analysis: combining textual, visual, and other modalities in social network data using machine learning for a comprehensive understanding of user interactions.

Dr. Dionisios Sotiropoulos
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• data mining
• machine learning
• recommendation systems
• social networks

Title: Visual Censorship: A Deep Learning-Based Approach to Prevent Leakage of Confidential Content in Images
Author: Paradise vit
Highlights: We investigate the ability of state-of-the-art deep learning methods to detect censored objects in images; We demonstrate the effectiveness of our method with real-world organizational data collected from Instagram. Images published by organization members contained censored objects, exposing the organization to potential security risks. This highlights the need for robust methods to detect and mitigate data leakage on OSNs.