Multimodal Attributed Graphs (MAGs) are ubiquitous in real-world applications, encompassing extensive knowledge through multimodal attributes attached to nodes (e.g., texts and images) and topological structure representing node interactions. Despite its potential to advance diverse research fields like social networks and e-commerce, MAG representation learning (MAGRL) remains underexplored due to the lack of standardized datasets and evaluation frameworks. In this paper, we first propose MAGB, a comprehensive MAG benchmark dataset, featuring curated graphs from various domains with both textual and visual attributes. Based on the MAGB dataset, we further systematically evaluate two mainstream MAGRL paradigms: GNN-as-Predictor, which integrates multimodal attributes via Graph Neural Networks (GNNs), and VLM-as-Predictor, which harnesses Vision Language Models (VLMs) for zero-shot reasoning. Extensive experiments on MAGB reveal the following critical insights: (i) Modality significances fluctuate drastically with specific domain characteristics. (ii) Multimodal embeddings can elevate the performance ceiling of GNNs. However, intrinsic biases among modalities may impede effective training, particularly in low-data scenarios. (iii) VLMs are highly effective at generating multimodal embeddings that alleviate the imbalance between textual and visual attributes. These discoveries, which illuminate the synergy between multimodal attributes and graph topologies, contribute to reliable benchmarks, paving the way for future research.

Owing to the unprecedented capability in semantic understanding and logical reasoning, large language models (LLMs) have shown fantastic potential in developing next-generation sequential recommender systems (RSs). However, existing LLM-based sequential RSs mostly separate index generation from sequential recommendation, leading to insufficient integration between semantic information and collaborative information. On the other hand, the neglect of user-related information hinders LLM-based sequential RSs from exploiting high-order user-item interaction patterns. In this paper, we propose the End-to-End Dual Dynamic (ED2) recommender, the first LLM-based sequential RS which adopts dual dynamic index mechanism, targeting resolving the above limitations simultaneously. The dual dynamic index mechanism can not only assembly index generation and sequential recommendation into a unified LLM-backbone pipeline, but also make it practical for LLM-based sequential recommender to take advantage of user-related information. Specifically, to facilitate the LLM comprehension ability to dual dynamic index, we propose a multigrained token regulator which constructs alignment supervision based on LLMs semantic knowledge across multiple representation granularities. Moreover, the associated user collection data and a series of novel instruction tuning tasks are specially customized to capture the high-order user-item interaction patterns. Extensive experiments on three public datasets demonstrate the superiority of ED2, achieving an average improvement of 19.62% in Hit-Rate and 21.11% in NDCG.

Self-supervised learning on graph neural networks is receiving increasing attention due to the difficulty of obtaining graph labels in many real applications. Graph contrastive learning (GCL), a recently popular method for self-supervised learning on graphs, has achieved great success in many tasks. The key to the effectiveness of GCL is the construction of suitable contrasting pairs to capture important attributes of the data through the data augmentation modules. However, most of the existing approaches fail to fully consider both data diversity and the semantic consistency when conducting data augmentation. To fill this gap, we propose an augmentation diversity and semantic consistency balanced graph contrastive learning model (ADSCB for short), which enhances the representation ability of the CL model through richer contrasting objectives. In particular, we first introduce a semantic consistency module to extract the subgraph from the original graph through optimizing a carefully designed semantic consistency loss. Then, we introduce an augmentation diversity module and perform data augmentation and cross-scale mix-up operations on the original graph and the extracted semantic preserved subgraph to generate more diverse contrasting pairs. With the above two modules, our model ultimately achieves two contrasting objectives: diversity contrasting and semantic contrasting. The tradeoff between these two contrasting objectives allows our model to benefit from both the augmentation diversity and the semantic consistency. We evaluate ADSCB for graph classification in unsupervised, semi-supervised, and transfer learning settings using standard graph contrastive learning benchmarks. The results demonstrate the superiority of our method against several state-of-the-art baselines.

Graph contrastive learning (GCL) has attracted rising research attention recently due to its effectiveness in self- supervised graph learning. A key step of GCL is to conduct data augmentation, based on which self-supervised learning is performed through the contrast between two augmented data views. Existing approaches generally generate the two data views from the original graph, which has been revealed to be less effective due to the lack of data diversity. Meanwhile, although the data augmentation methods and the contrastive modes have been extensively studied, the effect of hard negative samples (i.e.samples that are difficult to distinguish from an anchor node) on GCL is not fully explored. In this paper, we propose a novel complementary graph contrastive learning method boosted by adversarial hard negative sample generation. Specifically, we first construct a κNN graph as the complementary counterpart of the original graph in the semantic space. Then graph augmentation is conducted in both the semantic and topology spaces for the two complementary graphs to obtain two contrastive views with a larger data diversity. To facilitate the contrastive learning, an adversarial network named ADNet is also proposed to generate hard negative samples. The generated samples are more informative and challenging, and thus can further boost the learning performance. Extensive evaluations over the node classification task demonstrate that our proposal outperforms existing state-of-the-art GCL methods, and even exceeds supervised approaches. The code of this work is publicly available at https://github.com/sktsherlock/HNGCL-V1.