The empathetic dialogue systems aim to recognize user emotions and generate appropriate empathetic responses. However, existing approaches predominantly rely on dialogue history, contextual descriptions, and emotion category labels, failing to model the causal relationship between emotions and their underlying triggers. This limitation leads to generated responses that lack grounding, exhibit weak relevance, and suffer from poor interpretability in emotional expression. To address this, we propose MvP-ECR, a multi-perspective emotion cause reasoning framework that explicitly constructs emotion-cause structures to help models focus on the core emotional drivers. Additionally, we introduce an emotion-cause consistency evaluation metric to quantitatively assess a model’s ability to identify causal relationships. Experiments across multiple large language models (LLMs) demonstrate that the MvP-ECR framework can serve as a plug-and-play tool to help the model correctly infer emotions and causes in empathetic conversations, and provide more immersive responses for empathetic responses. All code and data will be publicly released to promote the development of empathy dialogue research.

The empathy dialogue system requires understanding emotions and their underlying causes.However, existing datasets mainly focus on emotion labels, while cause annotations are added post hoc through costly and subjective manual processes.This leads to three limitations: subjective bias in cause labels, weak rationality due to ambiguous cause-emotion relationships, and high annotation costs that hinder scalability.To address these challenges, we propose ECC (Emotion-Cause Conversation Dataset), a scalable dataset with 2.4K dialogues, which is also the first dialogue dataset where conversations and their emotion-cause labels are automatically generated synergistically during creation.We create an automatic extension framework EC-DD for ECC that utilizes knowledge and large language models (LLMs) to automatically generate conversations, and train a causality-aware empathetic response model CAER on this dataset.Experimental results show that ECC can achieve comparable or even superior performance to artificially constructed empathy dialogue datasets.

Social bots are becoming increasingly common in social networks, and their activities affect the security and authenticity of social media platforms. Current state-of-the-art social bot detection methods leverage multimodal approaches that analyze various modalities, such as user metadata, text, and social network relationships. However, these methods may not always extract additional dimensions of semantic feature information that could offer a deeper understanding of users' social patterns. To address this issue, we propose ETS-MM, a multimodal detection framework designed to augment multidimensional information from text and extract the semantic feature representation of user text information. We first analyze the user's tweeting behavior based on topic preference and emotion tendency, integrating them into the textual data. Then, we try to extract enhanced semantic representations that reveal the latent relationship between tweeting behavior and tweet content while identifying potential contextual associations and emotional changes. Additionally, to capture the complex interaction between users, we integrate the user's multimodal information, including metadata, textual features, enhanced semantic features, and social network relationships to propagate and aggregate information across various modalities. Experimental results demonstrate that ETS-MM significantly outperforms existing methods across two widely used social bot detection benchmark datasets, validating its effectiveness and superiority.

Multi-view clustering (MVC) can fuse the information of multiple views for robust clustering result, among it two fusion strategies,early-fusionandlate-fusionare widely adopted. Although they have derived many MVC methods, there are still two crucial questions: (1)early-fusionforces multiple views to share a consensus latent representation, which compounds the challenge of excavating view-specific diverse local information; (2)late-fusiongenerates view-partitions independently and then integrates them in the following clustering procedure, where the two procedures cannot guide each other and lack necessary negotiation. In view of this, we propose a novel Graph Proxy Fusion (GPF) method to preserve and fuse view-specific local information concertedly in one unified framework. Specifically, we first propose anchor-based local information learning to capture view-specific local structural information in bipartite graphs; meanwhile, a view-consensus graph learned through self-expressiveness-based proxy graph learning module is deemed as a higher-order proxy; following, the novel graph proxy fusion module integrally embeds all lower-order bipartite graphs in the higher-order proxy via higher-order correlation theory. As a novel fusion strategy, the proposed GPF efficiently investigates the valuable consensus and diverse information of multiple views. Experiments on various multi-view datasets demonstrate the superiority of our method.

Multiview clustering (MVC) can achieve more accurate results by utilizing complementary information from multiple perspectives, compared to traditional single-view methods. However, current multiview techniques require all views to be available upfront, making them inadequate for dealing with prevalent data sources that arrive as streams, such as stem cell analysis and multicamera surveillance. To address this problem, in this article, we propose a method called lifelong stream-view clustering (LSVC), which comprises an embedding anchor knowledge library and three key components, enabling the capability to perform asynchronous clustering on stream views. These three components are specifically: 1) the knowledge extraction module that extracts the abstract knowledge of the newcome view over time and updates the shared knowledge library; 2) the knowledge transfer module that aligns the newcome view with the historical knowledge library, enabling the transfer of structure information to the knowledge library; and 3) the knowledge rule module that constraints the knowledge library to enjoy a fair amount of anchors for each cluster, improving the discrimination of knowledge. The experimental results show that LSVC outperforms traditional single-view clustering (SVC) and MVC methods as it gradually improves with the accumulation of stream views and tends to be stable over time.

Graph learning can effectively characterize the similarity structure of sample pairs, hence multiple kernel clustering based on graph learning (MKC-GL) achieves promising results on nonlinear clustering tasks. However, previous methods confine to a “three-stage” scheme, that is, affinity graph learning, Laplacian construction, and clustering indicator extracting, which results in the information distortion in the step alternating. Meanwhile, the energy of Laplacian reconstruction and the necessary cluster information cannot be preserved simultaneously. To address these problems, we propose a one-stage shifted Laplacian refining (OSLR) method for multiple kernel clustering (MKC), where using the “one-stage” scheme focuses on Laplacian learning rather than traditional graph learning. Concretely, our method treats each kernel matrix as an affinity graph rather than ordinary data and constructs its corresponding Laplacian matrix in advance. Compared to the traditional Laplacian methods, we transform each Laplacian to an approximately shifted Laplacian (ASL) for refining a consensus Laplacian. Then, we project the consensus Laplacian onto a Fantope space to ensure that reconstruction information and clustering information concentrate on larger eigenvalues. Theoretically, our OSLR reduces the memory complexity and computation complexity to$O(n)$and$O(n^2)$, respectively. Moreover, experimental results have shown that it outperforms state-of-the-art MKC methods on multiple benchmark datasets.

Although multi-view clustering (MVC) has achieved remarkable performance by integrating the complementary information of views, it is inefficient when facing scalable data. Proverbially, anchor strategy can mitigate such a challenge a certain extent. However, the unsupervised dynamic strategy usually cannot obtain the optimal anchors for MVC. The main reasons are that it does not consider the fairness of different views and lacks the priori supervised guidance. To completely solve these problems, we first propose the priori anchor graph regularization (PAGG) for scalable multi-view bipartite graph clustering, dubbed as SMGC method. Specifically, SMGC learns a few representative consensus anchors to simulate the numerous view data well, and constructs a bipartite graph to bridge the affinities between the anchors and original data points. In order to largely improve the quality of anchors, PAGG predefines prior anchor labels to constrain the anchors with discriminative cluster structure and fair view allocation, such that a better bipartite graph can be obtained for fast clustering. Experimentally, abundant of experiments are accomplished on six scalable benchmark datasets, and the experimental results fully demonstrate the effectiveness and efficiency of our SMGC.

Graph-based clustering has become an active topic due to the efficiency in characterizing the relationships between the samples via graph. To improve the quality of graph, recent works propose to utilize global and local information. However, existing methods may lead to a degenerated graph when facing noisy and uneven distributed data. Since 1) they preserve the local information by referring the similarity between each sample-pair, whose confidence is easily disturbed by the poor quality samples; and 2) although the global information is relatively robust to the noisy, existing methods have island effect that lies between local and global structures learning, such that the information of both can not be utilized mutually. To alleviate these issues, this paper presents explicit local coupling global structure clustering (ELGSC) to explicitly learn the local structure and global structure information via a coupling scheme. To be specific, we learn$l(\ll n)$pseudo samples as the anchors to reflect local hot spots distribution, where$n$is the number of samples. By referring the relationship between each anchor-sample pair, ELGSC is capable of obtaining an effective local bipartite graph to capture the local structure. Meanwhile, the self-expressiveness learning is adopted to pursue a lower-rank global affinity graph. Finally, a higher-order coupling learning framework is proposed to couple the learning of global affinity graph and local bipartite graph. Thus, local and global structure information could be propagated each other on both graphs. The experimental results on real datasets demonstrate the efficacy of the proposed method over state-of-the-arts.

Multiple kernel clustering (MKC) has demonstrated promising performance for handing non-linear data clustering. Positively, it can integrate complementary information of multiple base kernels and avoid kernel function selection. However, negatively, the main challenging is that the kernel matrix with the size n x n leads to O(n2) memory complexity and O(n3) computational complexity. To mitigate such a challenging, taking graph Laplacian as breakthrough, this paper proposes a novel and simple MKC method, dubbed as approximate shifted Laplacian reconstruction (ASLR). For each base kernel, we propose the r-rank shifted Laplacian reconstruction scheme by considering the energy losing of Laplacian reconstruction and the clustering information preserving of Laplacian decompose simultaneously. Then, by analyzing the eigenvectors of the reconstructed Laplacian, we impose some constrains to tame its solution within a Fantope. Accordingly, the byproduct (i.e. the most informative eigenvectors) contains the main clustering information, such that the clustering assignments can be obtained relying on simple k-means algorithm. Owe to the Laplacian reconstruction scheme, the memory and computational complexity can be reduced to O(n) and O<(n^2)$, respectively. As experimentally demonstrated on eight challenging MKC benchmark datasets, the results verify the effectiveness and efficiency of ASLR.