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Yiqing Cai

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3 papers

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EAAI Journal 2026 Journal Article

BeLink: Behavior graph network for unsupervised user identity linkage

Xingkong Ma
Mengmeng Guo
Houjie Qiu
Yiqing Cai

With the development and rise of online social networks (OSNs), the user identity linkage (UIL) task has become a focal point in recent years, which aims to match accounts belonging to the same individual across different platforms. However, most efforts in UIL are based on labeled data or social graphs, which are frequently unavailable due to privacy or access constraints. Moreover, existing methods fail to address the need for fine-grained user behavior modeling for the UIL task. To address these challenges, we propose BeLink, an unsupervised UIL framework centered on a novel behavior graph construction mechanism. Our method is based on the insight of behavioral self-similarity that individuals often exhibit consistent behavior patterns across time and platforms. To capture behavioral consistency, we construct a user behavior graph for each pair of cross-platform users, where nodes represent short-term user activity segments, and edges encode their semantic correlations. To ensure an accurate representation of nodes, we first introduce a large language model (LLM) based textual apparent analysis that resolves cross-platform inconsistencies and unifies semantic content. Subsequently, we pretrain a behavior representation model on users’ self-behavior sequences to embed activities into a unified semantic space. To infer identity linkage, BeLink employs graph-based clustering and an entropy-weighted co-occurrence scoring mechanism. Experiments on real-world datasets demonstrate that BeLink effectively links user identities using only behavioral signals, achieving an average improvement of 11. 0% in hit rate at rank 1 (Hit@1) and 14. 7% in mean reciprocal rank (MRR), and consistently outperforms all baselines.

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AAAI Conference 2024 Conference Paper

Multi-Prototype Space Learning for Commonsense-Based Scene Graph Generation

Lianggangxu Chen
Youqi Song
Yiqing Cai
Jiale Lu
Yang Li
Yuan Xie
Changbo Wang
Gaoqi He

In the domain of scene graph generation, modeling commonsense as a single-prototype representation has been typically employed to facilitate the recognition of infrequent predicates. However, a fundamental challenge lies in the large intra-class variations of the visual appearance of predicates, resulting in subclasses within a predicate class. Such a challenge typically leads to the problem of misclassifying diverse predicates due to the rough predicate space clustering. In this paper, inspired by cognitive science, we maintain multi-prototype representations for each predicate class, which can accurately find the multiple class centers of the predicate space. Technically, we propose a novel multi-prototype learning framework consisting of three main steps: prototype-predicate matching, prototype updating, and prototype space optimization. We first design a triple-level optimal transport to match each predicate feature within the same class to a specific prototype. In addition, the prototypes are updated using momentum updating to find the class centers according to the matching results. Finally, we enhance the inter-class separability of the prototype space through iterations of the inter-class separability loss and intra-class compactness loss. Extensive evaluations demonstrate that our approach significantly outperforms state-of-the-art methods on the Visual Genome dataset.

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AAAI Conference 2023 Conference Paper

Explicit Invariant Feature Induced Cross-Domain Crowd Counting

Yiqing Cai
Lianggangxu Chen
Haoyue Guan
Shaohui Lin
Changhong Lu
Changbo Wang
Gaoqi He

Cross-domain crowd counting has shown progressively improved performance. However, most methods fail to explicitly consider the transferability of different features between source and target domains. In this paper, we propose an innovative explicit Invariant Feature induced Cross-domain Knowledge Transformation framework to address the inconsistent domain-invariant features of different domains. The main idea is to explicitly extract domain-invariant features from both source and target domains, which builds a bridge to transfer more rich knowledge between two domains. The framework consists of three parts, global feature decoupling (GFD), relation exploration and alignment (REA), and graph-guided knowledge enhancement (GKE). In the GFD module, domain-invariant features are efficiently decoupled from domain-specific ones in two domains, which allows the model to distinguish crowds features from backgrounds in the complex scenes. In the REA module both inter-domain relation graph (Inter-RG) and intra-domain relation graph (Intra-RG) are built. Specifically, Inter-RG aggregates multi-scale domain-invariant features between two domains and further aligns local-level invariant features. Intra-RG preserves taskrelated specific information to assist the domain alignment. Furthermore, GKE strategy models the confidence of pseudolabels to further enhance the adaptability of the target domain. Various experiments show our method achieves state-of-theart performance on the standard benchmarks. Code is available at https://github.com/caiyiqing/IF-CKT.

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