Four papers accepted in ACM CIKM 2024

1
Title: Empowering Traffic Speed Prediction with Auxiliary Feature-Aided Dependency Learning
Author: Dong-Hyuk Seo, Jiwon Son, Namhyuk Kim, Won-Yong Shin, Sang-Wook Kim
Abstract
Traffic speed prediction is a crucial task for optimizing navigation systems and reducing traffic congestion. Although there have been efforts to improve the accuracy of speed prediction by incorporating auxiliary features, such as traffic flow, weather, and time, types of auxiliary features are limited and their detailed relationships with speed have not been explored yet. In our study, we present the individual spatio-temporal (IST) dependencies on flow and speed, and characterize three types of IST-dependencies with the flow-to-flow, speed-to-speed, and flow-to-speed graphs. Then, we propose the Auxiliary feature-aided Attention Network (ARIAN), a novel approach to judiciously learning the degrees of IST-dependencies with the three graphs and predicting the future speed by leveraging various auxiliary features. Through comprehensive experiments using the 3 real-world datasets, we validate the superiority of ARIAN over 10 state-of-the-art methods and the effectiveness of each auxiliary feature and each dependency learner in ARIAN.

2
Title: Leveraging Trustworthy Node Attributes for Effective Network Alignment
Author: Dong-Hyuk Seo, Jaehwan Lim, Sang-Wook Kim
Abstract
With the prevalence of social media platforms, the need for accurately identifying the same users across different networks through network alignment has become increasingly crucial. Existing methods, which depend primarily on alignment consistency, assume that nodes with similar attributes and neighbors across networks are more likely to align. However, these methods often struggle due to the sparse or absent nature of user-identifiable information (node attributes) in reality, leading to the need for augmented attributes. Nevertheless, research on attribute augmentation remains largely under-explored, and existing methods often rely merely on the degree distribution of neighboring nodes, leading to sub-optimal performance.

To address the challenges, we established two critical claims for constructing effective augmented attributes: (C1) necessitates that attributes encode cross-network information to ensure accurate alignment across different networks; (C2) requires that identical elements across attribute vectors represent the same information to maintain consistency across networks. This paper introduces a novel approach to construct Augmented muLtI-aspect Cross-network Identical informAtion (ALICIA) attributes, which satisfies both (C1) and (C2). Building on this, the method leverages diverse structural relationship information from anchor nodes to construct trustworthy ALICIA attributes. We also present a gating mechanism that dynamically selects the most effective augmented attribute for enhancing alignment performance.

Through extensive experimentation across various datasets, we demonstrate that: 1) proposed network alignment framework utilize ALICIA attributes significantly to outperform state-of-the-art methods in terms of alignment accuracy; 2) ALICIA attributes are highly trustworthy, enabling effective alignment solely based on our attributes without any training process; and 3) ALICIA attributes consistently enhance the performance of state-of-the-art method when used as input augmented attribute.

3
Title: PolarDSN: An Inductive Approach to Learning the Evolution of Network Polarization in Dynamic Signed Networks
Author: Min-Jeong Kim, Yeon-Chang Lee, and Sang-Wook Kim
Abstract
The goal of dynamic signed network embedding (DSNE) is to represent the nodes in a dynamic signed network (DSN) as embeddings that preserve the evolving nature of conflicting relationships between nodes. While existing DSNE methods are useful for understanding polarization between users in diverse domains, they fail to consider the concept of a community boundary that contributes to network-wide polarization and lack inductive ability due to their reliance on homophily bias. To address these limitations, we propose a novel DSNE method, named PolarDSN, which learns the evolution of network POLARization and enhances inductive ability for Dynamic Signed Networks. It leverages node-level community boundaries as well as structural characteristics of nodes such as structural isomorphism and temporal transitivity. Experiments on four real-world DSN datasets demonstrate that PolarDSN consistently and significantly outperforms 12 state-of-the-art methods, achieving up to 31.6% and 21.1% improvement in macro-F1 for transductive and inductive settings, respectively

4
Title: Towards Fair Graph Anomaly Detection: Problem, New Datasets, and Evaluation
Author: Neng Kai Nigel Neo, Yeon-Chang Lee, Yiqiao Jin, Sang-Wook Kim, and Srijan Kumar
Abstract
The Fair Graph Anomaly Detection (FairGAD) problem aims to ac- curately detect anomalous nodes in an input graph while avoiding

biased predictions against individuals from sensitive subgroups. However, the current literature does not comprehensively discuss this problem, nor does it provide realistic datasets that encompass actual graph structures, anomaly labels, and sensitive attributes. To bridge this gap, we introduce a formal definition of the FairGAD problem and present two novel datasets constructed from the social media platforms Reddit and Twitter. These datasets comprise 1.2

million and 400,000 edges associated with 9,000 and 47,000 nodes, re- spectively, and leverage political leanings as sensitive attributes and

misinformation spreaders as anomaly labels. We demonstrate that our FairGAD datasets significantly differ from the synthetic datasets used by the research community. Using our datasets, we investigate

the performance-fairness trade-off in nine existing GAD and non- graph AD methods on five state-of-the-art fairness methods. Code

and datasets are available at https://github.com/nigelnnk/FairGAD.