Dynamic Graph Neural Ordinary Differential Equation Network for Multi-modal Emotion Recognition in Conversation

Multimodal emotion recognition in conversation (MERC) refers to identifying and classifying human emotional states by combining data from multiple different modalities (e.g., audio, images, text, video, etc.). Most existing multimodal emotion recognition methods use GCN to improve performance, but existing GCN methods are prone to overfitting and cannot capture the temporal dependency of the speaker's emotions. To address the above problems, we propose a Dynamic Graph Neural Ordinary Differential Equation Network (DGODE) for MERC, which combines the dynamic changes of emotions to capture the temporal dependency of speakers' emotions, and effectively alleviates the overfitting problem of GCNs. Technically, the key idea of DGODE is to utilize an adaptive mixhop mechanism to improve the generalization ability of GCNs and use the graph ODE evolution network to characterize the continuous dynamics of node representations over time and capture temporal dependencies. Extensive experiments on two publicly available multimodal emotion recognition datasets demonstrate that the proposed DGODE model has superior performance compared to various baselines. Furthermore, the proposed DGODE can also alleviate the over-smoothing problem, thereby enabling the construction of a deep GCN network.

SDR-GNN: Spectral Domain Reconstruction Graph Neural Network for Incomplete Multimodal Learning in Conversational Emotion Recognition

Multimodal Emotion Recognition in Conversations (MERC) aims to classify utterance emotions using textual, auditory, and visual modal features. Most existing MERC methods assume each utterance has complete modalities, overlooking the common issue of incomplete modalities in real-world scenarios. Recently, graph neural networks (GNNs) have achieved notable results in Incomplete Multimodal Emotion Recognition in Conversations (IMERC). However, traditional GNNs focus on binary relationships between nodes, limiting their ability to capture more complex, higher-order information. Moreover, repeated message passing can cause over-smoothing, reducing their capacity to preserve essential high-frequency details. To address these issues, we propose a Spectral Domain Reconstruction Graph Neural Network (SDR-GNN) for incomplete multimodal learning in conversational emotion recognition. SDR-GNN constructs an utterance semantic interaction graph using a sliding window based on both speaker and context relationships to model emotional dependencies. To capture higher-order and high-frequency information, SDR-GNN utilizes weighted relationship aggregation, ensuring consistent semantic feature extraction across utterances. Additionally, it performs multi-frequency aggregation in the spectral domain, enabling efficient recovery of incomplete modalities by extracting both high- and low-frequency information. Finally, multi-head attention is applied to fuse and optimize features for emotion recognition. Extensive experiments on various real-world datasets demonstrate that our approach is effective in incomplete multimodal learning and outperforms current state-of-the-art methods.

Contrastive Multi-graph Learning with Neighbor Hierarchical Sifting for Semi-supervised Text Classification

Graph contrastive learning has been successfully applied in text classification due to its remarkable ability for self-supervised node representation learning. However, explicit graph augmentations may lead to a loss of semantics in the contrastive views. Secondly, existing methods tend to overlook edge features and the varying significance of node features during multi-graph learning. Moreover, the contrastive loss suffer from false negatives. To address these limitations, we propose a novel method of contrastive multi-graph learning with neighbor hierarchical sifting for semi-supervised text classification, namely ConNHS. Specifically, we exploit core features to form a multi-relational text graph, enhancing semantic connections among texts. By separating text graphs, we provide diverse views for contrastive learning. Our approach ensures optimal preservation of the graph information, minimizing data loss and distortion. Then, we separately execute relation-aware propagation and cross-graph attention propagation, which effectively leverages the varying correlations between nodes and edge features while harmonising the information fusion across graphs. Subsequently, we present the neighbor hierarchical sifting loss (NHS) to refine the negative selection. For one thing, following the homophily assumption, NHS masks first-order neighbors of the anchor and positives from being negatives. For another, NHS excludes the high-order neighbors analogous to the anchor based on their similarities. Consequently, it effectively reduces the occurrence of false negatives, preventing the expansion of the distance between similar samples in the embedding space. Our experiments on ThuCNews, SogouNews, 20 Newsgroups, and Ohsumed datasets achieved 95.86\%, 97.52\%, 87.43\%, and 70.65\%, which demonstrates competitive results in semi-supervised text classification.

Graph Domain Adaptation with Dual-branch Encoder and Two-level Alignment for Whole Slide Image-based Survival Prediction

In recent years, histopathological whole slide image (WSI)- based survival analysis has attracted much attention in medical image analysis. In practice, WSIs usually come from different hospitals or laboratories, which can be seen as different domains, and thus may have significant differences in imaging equipment, processing procedures, and sample sources. These differences generally result in large gaps in distribution between different WSI domains, and thus the survival analysis models trained on one domain may fail to transfer to another. To address this issue, we propose a Dual-branch Encoder and Two-level Alignment (DETA) framework to explore both feature and category-level alignment between different WSI domains. Specifically, we first formulate the concerned problem as graph domain adaptation (GDA) by virtue the graph representation of WSIs. Then we construct a dual-branch graph encoder, including the message passing branch and the shortest path branch, to explicitly and implicitly extract semantic information from the graph-represented WSIs. To realize GDA, we propose a two-level alignment approach: at the category level, we develop a coupling technique by virtue of the dual-branch structure, leading to reduced divergence between the category distributions of the two domains; at the feature level, we introduce an adversarial perturbation strategy to better augment source domain feature, resulting in improved alignment in feature distribution. To the best of our knowledge, our work is the first attempt to alleviate the domain shift issue for WSI data analysis. Extensive experiments on four TCGA datasets have validated the effectiveness of our proposed DETA framework and demonstrated its superior performance in WSI-based survival analysis.

SEG:Seeds-Enhanced Iterative Refinement Graph Neural Network for Entity Alignment

Entity alignment is crucial for merging knowledge across knowledge graphs, as it matches entities with identical semantics. The standard method matches these entities based on their embedding similarities using semi-supervised learning. However, diverse data sources lead to non-isomorphic neighborhood structures for aligned entities, complicating alignment, especially for less common and sparsely connected entities. This paper presents a soft label propagation framework that integrates multi-source data and iterative seed enhancement, addressing scalability challenges in handling extensive datasets where scale computing excels. The framework uses seeds for anchoring and selects optimal relationship pairs to create soft labels rich in neighborhood features and semantic relationship data. A bidirectional weighted joint loss function is implemented, which reduces the distance between positive samples and differentially processes negative samples, taking into account the non-isomorphic neighborhood structures. Our method outperforms existing semi-supervised approaches, as evidenced by superior results on multiple datasets, significantly improving the quality of entity alignment.

Graph Contrastive Learning via Cluster-refined Negative Sampling for Semi-supervised Text Classification

Graph contrastive learning (GCL) has been widely applied to text classification tasks due to its ability to generate self-supervised signals from unlabeled data, thus facilitating model training. However, existing GCL-based text classification methods often suffer from negative sampling bias, where similar nodes are incorrectly paired as negative pairs. This can lead to over-clustering, where instances of the same class are divided into different clusters. To address the over-clustering issue, we propose an innovative GCL-based method of graph contrastive learning via cluster-refined negative sampling for semi-supervised text classification, namely ClusterText. Firstly, we combine the pre-trained model Bert with graph neural networks to learn text representations. Secondly, we introduce a clustering refinement strategy, which clusters the learned text representations to obtain pseudo labels. For each text node, its negative sample set is drawn from different clusters. Additionally, we propose a self-correction mechanism to mitigate the loss of true negative samples caused by clustering inconsistency. By calculating the Euclidean distance between each text node and other nodes within the same cluster, distant nodes are still selected as negative samples. Our proposed ClusterText demonstrates good scalable computing, as it can effectively extract important information from from a large amount of data. Experimental results demonstrate the superiority of ClusterText in text classification tasks.

MCSFF: Multi-modal Consistency and Specificity Fusion Framework for Entity Alignment

Multi-modal entity alignment (MMEA) is essential for enhancing knowledge graphs and improving information retrieval and question-answering systems. Existing methods often focus on integrating modalities through their complementarity but overlook the specificity of each modality, which can obscure crucial features and reduce alignment accuracy. To solve this, we propose the Multi-modal Consistency and Specificity Fusion Framework (MCSFF), which innovatively integrates both complementary and specific aspects of modalities. We utilize Scale Computing's hyper-converged infrastructure to optimize IT management and resource allocation in large-scale data processing. Our framework first computes similarity matrices for each modality using modality embeddings to preserve their unique characteristics. Then, an iterative update method denoises and enhances modality features to fully express critical information. Finally, we integrate the updated information from all modalities to create enriched and precise entity representations. Experiments show our method outperforms current state-of-the-art MMEA baselines on the MMKG dataset, demonstrating its effectiveness and practical potential.

SpeGCL: Self-supervised Graph Spectrum Contrastive Learning without Positive Samples

Graph Contrastive Learning (GCL) excels at managing noise and fluctuations in input data, making it popular in various fields (e.g., social networks, and knowledge graphs). Our study finds that the difference in high-frequency information between augmented graphs is greater than that in low-frequency information. However, most existing GCL methods focus mainly on the time domain (low-frequency information) for node feature representations and cannot make good use of high-frequency information to speed up model convergence. Furthermore, existing GCL paradigms optimize graph embedding representations by pulling the distance between positive sample pairs closer and pushing the distance between positive and negative sample pairs farther away, but our theoretical analysis shows that graph contrastive learning benefits from pushing negative pairs farther away rather than pulling positive pairs closer. To solve the above-mentioned problems, we propose a novel spectral GCL framework without positive samples, named SpeGCL. Specifically, to solve the problem that existing GCL methods cannot utilize high-frequency information, SpeGCL uses a Fourier transform to extract high-frequency and low-frequency information of node features, and constructs a contrastive learning mechanism in a Fourier space to obtain better node feature representation. Furthermore, SpeGCL relies entirely on negative samples to refine the graph embedding. We also provide a theoretical justification for the efficacy of using only negative samples in SpeGCL. Extensive experiments on un-supervised learning, transfer learning, and semi-supervised learning have validated the superiority of our SpeGCL framework over the state-of-the-art GCL methods.

Contrastive Graph Representation Learning with Adversarial Cross-view Reconstruction and Information Bottleneck

Graph Neural Networks (GNNs) have received extensive research attention due to their powerful information aggregation capabilities. Despite the success of GNNs, most of them suffer from the popularity bias issue in a graph caused by a small number of popular categories. Additionally, real graph datasets always contain incorrect node labels, which hinders GNNs from learning effective node representations. Graph contrastive learning (GCL) has been shown to be effective in solving the above problems for node classification tasks. Most existing GCL methods are implemented by randomly removing edges and nodes to create multiple contrasting views, and then maximizing the mutual information (MI) between these contrasting views to improve the node feature representation. However, maximizing the mutual information between multiple contrasting views may lead the model to learn some redundant information irrelevant to the node classification task. To tackle this issue, we propose an effective Contrastive Graph Representation Learning with Adversarial Cross-view Reconstruction and Information Bottleneck (CGRL) for node classification, which can adaptively learn to mask the nodes and edges in the graph to obtain the optimal graph structure representation. Furthermore, we innovatively introduce the information bottleneck theory into GCLs to remove redundant information in multiple contrasting views while retaining as much information as possible about node classification. Moreover, we add noise perturbations to the original views and reconstruct the augmented views by constructing adversarial views to improve the robustness of node feature representation. Extensive experiments on real-world public datasets demonstrate that our method significantly outperforms existing state-of-the-art algorithms.

A Multi-view Mask Contrastive Learning Graph Convolutional Neural Network for Age Estimation

The age estimation task aims to use facial features to predict the age of people and is widely used in public security, marketing, identification, and other fields. However, the features are mainly concentrated in facial keypoints, and existing CNN and Transformer-based methods have inflexibility and redundancy for modeling complex irregular structures. Therefore, this paper proposes a Multi-view Mask Contrastive Learning Graph Convolutional Neural Network (MMCL-GCN) for age estimation. Specifically, the overall structure of the MMCL-GCN network contains a feature extraction stage and an age estimation stage. In the feature extraction stage, we introduce a graph structure to construct face images as input and then design a Multi-view Mask Contrastive Learning (MMCL) mechanism to learn complex structural and semantic information about face images. The learning mechanism employs an asymmetric siamese network architecture, which utilizes an online encoder-decoder structure to reconstruct the missing information from the original graph and utilizes the target encoder to learn latent representations for contrastive learning. Furthermore, to promote the two learning mechanisms better compatible and complementary, we adopt two augmentation strategies and optimize the joint losses. In the age estimation stage, we design a Multi-layer Extreme Learning Machine (ML-IELM) with identity mapping to fully use the features extracted by the online encoder. Then, a classifier and a regressor were constructed based on ML-IELM, which were used to identify the age grouping interval and accurately estimate the final age. Extensive experiments show that MMCL-GCN can effectively reduce the error of age estimation on benchmark datasets such as Adience, MORPH-II, and LAP-2016.