The rapid rise of online social networks underscores the need to understand the heterogeneous strengths of online relationships. Yet, efforts to assess tie strength (TS) are hindered by the lack of ground-truth labels, differing research perspectives, and limited model performance in real-world settings. To address this gap, we introduce BTS, a comprehensive Benchmark for Tie Strength prediction, aiming to establish a standardized foundation for evaluating and advancing TS prediction methodologies. Specifically, our contributions are: TS Pseudo-Label Techniques -- we categorize TS into seven standardized pseudo-labeling techniques based on prior literature; TS Dataset Collection -- we present a representative collection of three social networks and perform data analysis by investigating the class distributions and correlations across the generated pseudo-labels; TS Pseudo-Label Evaluation Framework -- we propose a standardized framework to evaluate the pseudo-label quality from the perspective of tie resilience; Benchmarking -- we evaluate existing tie strength prediction model performance using the BTS dataset collection, exploring the effects of different experiment settings, models, and evaluation criteria on the results. Furthermore, we derive key insights to enhance existing methods and shed light on promising directions for future research in this domain. The BTS dataset collection, along with the curation codes and experimental scripts, is all available at: https://github.com/XueqiC/Awesome-Tie-Strength-Prediction.

Recent years have witnessed the remarkable success of applying Graph Machine Learning (GML) to node/graph classification and link prediction. However, edge classification task that enjoys numerous real-world applications such as social network analysis and cybersecurity, has not seen significant advancement with the progress of GML. To address this gap, our study pioneers a comprehensive approach to edge classification. We identify a novel 'Topological Imbalance Issue,' which arises from the skewed distribution of edges across different classes, affecting the local subgraph of each edge and harming the performance of edge classifications. Inspired by recent node-level studies observing performance discrepancies with varying local structural patterns, we aim to investigate if the topological imbalanced edge classification tasks can also be mitigated by characterizing the local class distribution variance. Thus, we introduce Topological Entropy (TE), a novel topological-based metric that measures the topological imbalance for each edge. Our empirical studies confirm that TE effectively measures local class distribution variance, and indicate that prioritizing edges with high TE values can help address the issue of topological imbalance. Inspired by this observation, we develop two strategies - Topological Reweighting and TE Wedge-based Mixup - to adaptively focus training on (synthetic) edges based on their TEs. While topological reweighting directly manipulates training edge weights according to TE, our wedge-based mixup interpolates synthetic edges between high TE wedges. To further enhance performance, we integrate these strategies into a novel topological imbalance strategy for edge classification: TopoEdge. Extensive experiments on real-world datasets demonstrate the efficacy of our proposed strategies. Our code and data are available at https://github.com/XueqiC/TopoEdge. Additionally, our curated datasets and designed experimental settings establish a new benchmark for future edge classification research, particularly in addressing imbalance issues.

Network analysis has evolved substantially, with notable advancements in node-centric and graph-centric tasks, yet the exploration of edge-centric analytics has been notably limited. This oversight is significant given the crucial role of edges in elucidating the complex relationships within networks, particularly in fields such as social network analysis, cybersecurity, and bioinformatics, where the dynamics of connections between entities are often pivotal. My doctoral research aims to address this gap by delving into the under-explored domain of edge-centric analytics, providing a foundational background that is crucial for advancing the field and enhancing the application of network theory in real-world scenarios. The significance of this research lies in its potential to open new avenues for inquiry and application across diverse disciplines where understanding the nuances of relational dynamics is essential.

Recommender systems (RS) are effective tools for mitigating information overload and have seen extensive applications across various domains. However, the single focus on utility goals proves to be inadequate in addressing real-world concerns, leading to increasing attention to fairness-aware and diversity-aware RS. While most existing studies explore fairness and diversity independently, we identify strong connections between these two domains. In this survey, we first discuss each of them individually and then dive into their connections. Additionally, motivated by the concepts of user-level and item-level fairness, we broaden the understanding of diversity to encompass not only the item level but also the user level. With this expanded perspective on user and item-level diversity, we re-interpret fairness studies from the viewpoint of diversity. This fresh perspective enhances our understanding of fairness-related work and paves the way for potential future research directions. Articles discussed in this survey along with public code links are available at: https://github.com/YuyingZhao/Awesome-Fairness-and-Diversity-Papers-in-Recommender-Systems

Graph Neural Networks (GNNs) have gained significant attention owing to their ability to handle graph-structured data and the improvement in practical applications. However, many of these models prioritize high utility performance, such as accuracy, with a lack of privacy consideration, which is a major concern in modern society where privacy attacks are rampant. To address this issue, researchers have started to develop privacy-preserving GNNs. Despite this progress, there is a lack of a comprehensive overview of the attacks and the techniques for preserving privacy in the graph domain. In this survey, we aim to address this gap by summarizing the attacks on graph data according to the targeted information, categorizing the privacy preservation techniques in GNNs, and reviewing the datasets and applications that could be used for analyzing/solving privacy issues in GNNs. We also outline potential directions for future research in order to build better privacy-preserving GNNs.