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Jianwu Dang

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15 papers

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AAAI Conference 2025 Conference Paper

Enriching Multimodal Sentiment Analysis Through Textual Emotional Descriptions of Visual-Audio Content

Sheng Wu
Dongxiao He
Xiaobao Wang
Longbiao Wang
Jianwu Dang

Multimodal Sentiment Analysis (MSA) stands as a critical research frontier, seeking to comprehensively unravel human emotions by amalgamating text, audio, and visual data. Yet, discerning subtle emotional nuances within audio and video expressions poses a formidable challenge, particularly when emotional polarities across various segments appear similar. In this paper, our objective is to spotlight emotion-relevant attributes of audio and visual modalities to facilitate multimodal fusion in the context of nuanced emotional shifts in visual-audio scenarios. To this end, we introduce DEVA, a progressive fusion framework founded on textual sentiment descriptions aimed at accentuating emotional features of visual-audio content. DEVA employs an Emotional Description Generator (EDG) to transmute raw audio and visual data into textualized sentiment descriptions, thereby amplifying their emotional characteristics. These descriptions are then integrated with the source data to yield richer, enhanced features. Furthermore, DEVA incorporates the Text-guided Progressive Fusion Module (TPF), leveraging varying levels of text as a core modality guide. This module progressively fuses visual-audio minor modalities to alleviate disparities between text and visual-audio modalities. Experimental results on widely used sentiment analysis benchmark datasets, including MOSI, MOSEI, and CH-SIMS, underscore significant enhancements compared to state-of-the-art models. Moreover, fine-grained emotion experiments corroborate the robust sensitivity of DEVA to subtle emotional variations.

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AAAI Conference 2025 Conference Paper

HeterGP: Bridging Heterogeneity in Graph Neural Networks with Multi-View Prompting

Fengyu Yan
Xiaobao Wang
Dongxiao He
Longbiao Wang
Jianwu Dang
Di Jin

The challenges tied to unstructured graph data are manifold, primarily falling into node, edge, and graph-level problem categories. Graph Neural Networks (GNNs) serve as effective tools to tackle these issues. However, individual tasks often demand distinct model architectures, and training these models typically requires abundant labeled data, a luxury often unavailable in practical settings. Recently, various "prompt tuning" methodologies have emerged to empower GNNs to adapt to multi-task learning with limited labels. The crux of these methods lies in bridging the gap between pre-training tasks and downstream objectives. Nonetheless, a prevalent oversight in existing studies is the homophily-centric nature of prompt tuning frameworks, disregarding scenarios characterized by high heterogeneity. To remedy this oversight, we introduce a novel prompting strategy named HeterGP tailored for highly heterophilic scenarios. Specifically, we present a dual-view approach to capture both homophilic and heterophilic information, along with a prompt graph design that encompasses token initialization and insertion patterns. Through extensive experiments conducted in a few-shot context encompassing node and graph classification tasks, our method showcases superior performance in highly heterophilic environments compared to state-of-the-art prompt tuning techniques.

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TIST Journal 2025 Journal Article

Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning

Cuiying Huo
Dongxiao He
Yawen Li
Di Jin
Jianwu Dang
Witold Pedrycz
Lingfei Wu
Weixiong Zhang

Heterogeneous graph neural network (HGNN) is a popular technique for modeling and analyzing heterogeneous graphs. Most existing HGNN-based approaches are supervised or semi-supervised learning methods requiring graphs to be annotated, which is costly and time-consuming. Self-supervised contrastive learning has been proposed to address the problem of requiring annotated data by mining intrinsic properties in the given data. However, the existing contrastive learning methods are not suitable for heterogeneous graphs because they construct contrastive views only based on data perturbation or pre-defined structural properties (e.g., meta-path) in graph data while ignoring noises in node attributes and graph topologies. We develop a robust heterogeneous graph contrastive learning approach, namely HGCL, which introduces two views on respective guidances of node attributes and graph topologies and integrates and enhances them by a reciprocally contrastive mechanism to better model heterogeneous graphs. In this new approach, we adopt distinct but suitable attribute and topology fusion mechanisms in the two views, which are conducive to mining relevant information in attributes and topologies separately. We further use both attribute similarity and topological correlation to construct high-quality contrastive samples. Extensive experiments on four large real-world heterogeneous graphs demonstrate the superiority and robustness of HGCL over several state-of-the-art methods.

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IJCAI Conference 2025 Conference Paper

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework

Xiao Wei
Xiaobao Wang
Ning Zhuang
Chenyang Wang
Longbiao Wang
Jianwu Dang

Intent detection aims to identify user intents from natural language inputs, where supervised methods rely heavily on labeled in-domain (IND) data and struggle with out-of-domain (OOD) intents, limiting their practical applicability. Generalized Intent Discovery (GID) addresses this by leveraging unlabeled OOD data to discover new intents without additional annotation. However, existing methods focus solely on clustering unsupervised data while neglecting domain adaptation. Therefore, we propose a consistency-driven prototype-prompting framework for GID from the perspective of integrating old and new knowledge, which includes a prototype-prompting framework for transferring old knowledge from external sources, and a hierarchical consistency constraint for learning new knowledge from target domains. We conducted extensive experiments and the results show that our method significantly outperforms all baseline methods, achieving state-of-the-art results, which strongly demonstrates the effectiveness and generalization of our methods. Our source code is publicly available at https: //github. com/smileix/cpp.

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IJCAI Conference 2025 Conference Paper

Rethinking Contrastive Learning in Graph Anomaly Detection: A Clean-View Perspective

Di Jin
Jingyi Cao
Xiaobao Wang
Bingdao Feng
Dongxiao He
Longbiao Wang
Jianwu Dang

Graph anomaly detection aims to identify unusual patterns in graph-based data, with wide applications in fields such as web security and financial fraud detection. Existing methods typically rely on contrastive learning, assuming that a lower similarity between a node and its local subgraph indicates abnormality. However, these approaches overlook a crucial limitation: the presence of interfering edges invalidates this assumption, since it introduces disruptive noise that compromises the contrastive learning process. Consequently, this limitation impairs the ability to effectively learn meaningful representations of normal patterns, leading to suboptimal detection performance. To address this issue, we propose a Clean-View Enhanced Graph Anomaly Detection framework (CVGAD), which includes a multi-scale anomaly awareness module to identify key sources of interference in the contrastive learning process. Moreover, to mitigate bias from the one-step edge removal process, we introduce a novel progressive purification module. This module incrementally refines the graph by iteratively identifying and removing interfering edges, thereby enhancing model performance. Extensive experiments on five benchmark datasets validate the effectiveness of our approach.

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EAAI Journal 2025 Journal Article

TSPCS-net: Two-stage pavement crack segmentation network based on encoder-decoder architecture

Biao Yue
Jianwu Dang
Qi Sun
Yangping Wang
Yongzhi Min
Feng Wang

Crack segmentation is of great significance in automatic pavement crack detection based on image recognition. Although recent convolutional neural network (CNN)-based segmentation methods have shown promising performance, accurate pavement crack segmentation still faces some challenges, such as various crack sizes, class imbalance issues, and background interference. To overcome these challenges, a compact two-stage pavement crack segmentation network based on encoder-decoder architecture (TSPCS-Net) is proposed, which includes a classification network and a segmentation network. The classification network, consisting of a feature extraction module transferred from the segmentation network and a lightweight feature fusion module, is used to quickly classify and eliminate crack-free images that existed in large numbers in actual pavement image datasets. The segmentation network is constructed based on an encoder-decoder architecture for precise pixel-level segmentation of the samples determined as crack images. Specifically, to extract multi-scale crack features, a novel multi-scale encoder module is designed by combining dilated convolution and residual structure. Then, a left-side path (LSP) is designed to alleviate the influence of class imbalance on feature extraction. Finally, an attention module with high-dimensional features guiding low-dimensional features (AM-HGL) is proposed to focus on crack-relevant features and suppress interference information. The effectiveness of the proposed TSPCS-Net is validated on a self-made unmanned aerial vehicles pavement crack (UAVPC) dataset and two public pavement distress datasets, and extensive experiments show that the proposed method outperforms current state-of-the-art methods in terms of segmentation performance and efficiency, which can meet the needs of pavement crack segmentation in practical application scenarios.

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NeurIPS Conference 2025 Conference Paper

Word-Level Emotional Expression Control in Zero-Shot Text-to-Speech Synthesis

Tianrui Wang
Haoyu Wang
Meng Ge
Cheng Gong
Chunyu Qiang
Ziyang Ma
Zikang Huang
Guanrou Yang

While emotional text-to-speech (TTS) has made significant progress, most existing research remains limited to utterance-level emotional expression and fails to support word-level control. Achieving word-level expressive control poses fundamental challenges, primarily due to the complexity of modeling multi-emotion transitions and the scarcity of annotated datasets that capture intra-sentence emotional and prosodic variation. In this paper, we propose WeSCon, the first self-training framework that enables word-level control of both emotion and speaking rate in a pretrained zero-shot TTS model, without relying on datasets containing intra-sentence emotion or speed transitions. Our method introduces a transition-smoothing strategy and a dynamic speed control mechanism to guide the pretrained TTS model in performing word-level expressive synthesis through a multi-round inference process. To further simplify the inference, we incorporate a dynamic emotional attention bias mechanism and fine-tune the model via self-training, thereby activating its ability for word-level expressive control in an end-to-end manner. Experimental results show that WeSCon effectively overcomes data scarcity, achieving state-of-the-art performance in word-level emotional expression control while preserving the strong zero-shot synthesis capabilities of the original TTS model.

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AAAI Conference 2024 Conference Paper

Ahpatron: A New Budgeted Online Kernel Learning Machine with Tighter Mistake Bound

Yun Liao
Junfan Li
Shizhong Liao
Qinghua Hu
Jianwu Dang

In this paper, we study the mistake bound of online kernel learning on a budget. We propose a new budgeted online kernel learning model, called Ahpatron, which significantly improves the mistake bound of previous work and resolves an open problem related to upper bounds of hypothesis space constraints. We first present an aggressive variant of Perceptron, named AVP, a model without budget, which uses an active updating rule. Then we design a new budget maintenance mechanism, which removes a half of examples, and projects the removed examples onto a hypothesis space spanned by the remaining examples. Ahpatron adopts the above mechanism to approximate AVP. Theoretical analyses prove that Ahpatron has tighter mistake bounds, and experimental results show that Ahpatron outperforms the state-of-the-art algorithms on the same or a smaller budget.

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AAAI Conference 2023 Conference Paper

Augmenting Affective Dependency Graph via Iterative Incongruity Graph Learning for Sarcasm Detection

Xiaobao Wang
Yiqi Dong
Di Jin
Yawen Li
Longbiao Wang
Jianwu Dang

Recently, progress has been made towards improving automatic sarcasm detection in computer science. Among existing models, manually constructing static graphs for texts and then using graph neural networks (GNNs) is one of the most effective approaches for drawing long-range incongruity patterns. However, the manually constructed graph structure might be prone to errors (e.g., noisy or incomplete) and not optimal for the sarcasm detection task. Errors produced during the graph construction step cannot be remedied and may accrue to the following stages, resulting in poor performance. To surmount the above limitations, we explore a novel Iterative Augmenting Affective Graph and Dependency Graph (IAAD) framework to jointly and iteratively learn the incongruity graph structure. IAAD can alternatively update the incongruity graph structure and node representation until the learning graph structure is optimal for the metrics of sarcasm detection. More concretely, we begin with deriving an affective and a dependency graph for each instance, then an iterative incongruity graph learning module is employed to augment affective and dependency graphs for obtaining the optimal inconsistent semantic graph with the goal of optimizing the graph for the sarcasm detection task. Extensive experiments on three datasets demonstrate that the proposed model outperforms state-of-the-art baselines for sarcasm detection with significant margins.

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IJCAI Conference 2023 Conference Paper

Commonsense Knowledge Enhanced Sentiment Dependency Graph for Sarcasm Detection

Zhe Yu
Di Jin
Xiaobao Wang
Yawen Li
Longbiao Wang
Jianwu Dang

Sarcasm is widely utilized on social media platforms such as Twitter and Reddit. Sarcasm detection is required for analyzing people's true feelings since sarcasm is commonly used to portray a reversed emotion opposing the literal meaning. The syntactic structure is the key to make better use of commonsense when detecting sarcasm. However, it is extremely challenging to effectively and explicitly explore the information implied in syntactic structure and commonsense simultaneously. In this paper, we apply the pre-trained COMET model to generate relevant commonsense knowledge, and explore a novel scenario of constructing a commonsense-augmented sentiment graph and a commonsense-replaced dependency graph for each text. Based on this, a Commonsense Sentiment Dependency Graph Convolutional Network (CSDGCN) framework is proposed to explicitly depict the role of external commonsense and inconsistent expressions over the context for sarcasm detection by interactively modeling the sentiment and dependency information. Experimental results on several benchmark datasets reveal that our proposed method beats the state-of-the-art methods in sarcasm detection, and has a stronger interpretability.

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AAAI Conference 2020 Conference Paper

Topic Enhanced Sentiment Spreading Model in Social Networks Considering User Interest

Xiaobao Wang
Di Jin
Katarzyna Musial
Jianwu Dang

Emotion is a complex emotional state, which can affect our physiology and psychology and lead to behavior changes. The spreading process of emotions in the text-based social networks is referred to as sentiment spreading. In this paper, we study an interesting problem of sentiment spreading in social networks. In particular, by employing a text-based social network (Twitter), we try to unveil the correlation between users’ sentimental statuses and topic distributions embedded in the tweets, then to automatically learn the inﬂuence strength between linked users. Furthermore, we introduce user interest to reﬁne the inﬂuence strength. We develop a uniﬁed probabilistic framework to formalize the problem into a topic-enhanced sentiment spreading model. The model can predict users’ sentimental statuses based on their historical emotional status, topic distributions in tweets and social structures. Experiments on the Twitter dataset show that the proposed model signiﬁcantly outperforms several alternative methods in predicting users’ sentimental status. We also discover an intriguing phenomenon that positive and negative sentiment is more relevant to user interest than neutral ones. Our method offers a new opportunity to understand the underlying mechanism of sentimental spreading in online social networks.

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AAAI Conference 2020 Conference Paper

Working Memory-Driven Neural Networks with a Novel Knowledge Enhancement Paradigm for Implicit Discourse Relation Recognition

Fengyu Guo
Ruifang He
Jianwu Dang
Jian Wang

Recognizing implicit discourse relation is a challenging task in discourse analysis, which aims to understand and infer the latent relations between two discourse arguments, such as temporal, comparison. Most of the present models largely focus on learning-based methods that utilize only intra-sentence textual information to identify discourse relations, ignoring the wider contexts beyond the discourse. Moreover, people comprehend the meanings and the relations of discourses, heavily relying on their interconnected working memories (e. g. , instant memory, long-term memory). Inspired by this, we propose a Knowledge-Enhanced Attentive Neural Network (KANN) framework to address these issues. Speciﬁcally, it establishes a mutual attention matrix to capture the reciprocal information between two arguments, as instant memory. While implicitly stated knowledge in the arguments is retrieved from external knowledge source and encoded as inter-words semantic connection embeddings to further construct knowledge matrix, as long-term memory. We devise a novel paradigm with two ways by the collaboration of the memories to enrich the argument representation: 1) integrating the knowledge matrix into the mutual attention matrix, which implicitly maps knowledge into the process of capturing asymmetric interactions between two discourse arguments; 2) directly concatenating the argument representations and the semantic connection embeddings, which explicitly supplements knowledge to help discourse understanding. The experimental results on the PDTB also show that our KANN model is effective.

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AAAI Conference 2019 Conference Paper

Community Detection in Social Networks Considering Topic Correlations

Yingkui Wang
Di Jin
Katarzyna Musial
Jianwu Dang

Network contents including node contents and edge contents can be utilized for community detection in social networks. Thus, the topic of each community can be extracted as its semantic information. A plethora of models integrating topic model and network topologies have been proposed. However, a key problem has not been resolved that is the semantic division of a community. Since the definition of community is based on topology, a community might involve several topics. To achieve better community detection results and to better understand the fundamental community semantics, we investigate the correlations of different topics in community detection model. This work models the formation of each edge assuming that users are more likely to communicate with each other when they are in the same community and their topics are closely correlated. A Topic Correlations based Community Detection (TCCD) model is proposed, which can learn community structure and semantic interpretation of each community. Our model is evaluated on two real datasets and is compared with four state-of-the-art methods. Experimental results show that TCCD significantly improves the accuracy of community detection. Finally, a case study shows that TCCD can detect the topic correlations inside a community. And we can infer better semantic interpretation of each community.

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AAAI Conference 2018 Conference Paper

Robust Detection of Link Communities in Large Social Networks by Exploiting Link Semantics

Di Jin
Xiaobao Wang
Ruifang He
Dongxiao He
Jianwu Dang
Weixiong Zhang

Community detection has been extensively studied for various applications, focusing primarily on network topologies. Recent research has started to explore node contents to identify semantically meaningful communities and interpret their structures using selected words. However, links in real networks typically have semantic descriptions, e.g., comments and emails in social media, supporting the notion of communities of links. Indeed, communities of links can better describe multiple roles that nodes may play and provide a richer characterization of community behaviors than communities of nodes. The second issue in community finding is that most existing methods assume network topologies and descriptive contents to be consistent and to carry the compatible information of node group membership, which is generally violated in real networks. These methods are also restricted to interpret one community with one topic. The third problem is that the existing methods have used top ranked words or phrases to label topics when interpreting communities. However, it is often difficult to comprehend the derived topics using words or phrases, which may be irrelevant. To address these issues altogether, we propose a new unified probabilistic model that can be learned by a dual nested expectation-maximization algorithm. Our new method explores the intrinsic correlation between communities and topics to discover link communities robustly and extract adequate community summaries in sentences instead of words for topic labeling at the same time. It is able to derive more than one topical summary per community to provide rich explanations. We present experimental results to show the effectiveness of our new approach, and evaluate the quality of the results by a case study.

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AAAI Conference 2016 Conference Paper

Detect Overlapping Communities via Ranking Node Popularities

Di Jin
Hongcui Wang
Jianwu Dang
Dongxiao He
Weixiong Zhang

Detection of overlapping communities has drawn much attention lately as they are essential properties of real complex networks. Despite its influence and popularity, the well studied and widely adopted stochastic model has not been made effective for finding overlapping communities. Here we extend the stochastic model method to detection of overlapping communities with the virtue of autonomous determination of the number of communities. Our approach hinges upon the idea of ranking node popularities within communities and using a Bayesian method to shrink communities to optimize an objective function based on the stochastic generative model. We evaluated the novel approach, showing its superior performance over five state-of-the-art methods, on large real networks and synthetic networks with ground-truths of overlapping communities.

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