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Works572 from public data
- Structural Deep Network Embedding2,836
This paper proposes a Structural Deep Network Embedding method, namely SDNE, which first proposes a semi-supervised deep model, which has multiple layers of non-linear functions, thereby being able to capture the highly non- linear network structure and exploits the first-order and second-order proximity jointly to preserve the network structure.
- Deep Learning on Graphs: A Survey1,644
This survey comprehensively review the different types of deep learning methods on graphs by dividing the existing methods into five categories based on their model architectures and training strategies: graph recurrent neural networks, graph convolutional networks,graph autoencoders, graph reinforcement learning, and graph adversarial methods.
- Asymmetric Transitivity Preserving Graph Embedding1,333
A novel graph embedding algorithm, High-Order Proximity preserved Embedding (HOPE for short), is developed, which is scalable to preserve high-order proximities of large scale graphs and capable of capturing the asymmetric transitivity.
- A Survey on Network Embedding1,269
This survey focuses on categorizing and then reviewing the current development on network embedding methods, and point out its future research directions, covering the structure- and property-preserving network embeding methods, the network embedded methods with side information, and the advanced information preserving network embedting methods.
- 1,073
- Community Preserving Network Embedding935
A novel Modularized Nonnegative Matrix Factorization (M-NMF) model is proposed to incorporate the community structure into network embedding and jointly optimize NMF based representation learning model and modularity based community detection model in a unified framework, which enables the learned representations of nodes to preserve both of the microscopic and community structures.
- Robust Graph Convolutional Networks Against Adversarial Attacks580
Robust GCN (RGCN), a novel model that "fortifies'' GCNs against adversarial attacks by adopting Gaussian distributions as the hidden representations of nodes in each convolutional layer, which can automatically absorb the effects of adversarial changes in the variances of the Gaussian distribution.
- 473
- Depression Detection via Harvesting Social Media: A Multimodal Dictionary Learning Solution404
A multimodal depressive dictionary learning model is proposed to detect the depressed users on Twitter and a series of experiments are conducted to validate this model, which outperforms (+3% to +10%) several baselines.
- To Find Where You Talk: Temporal Sentence Localization in Video with Attention Based Location Regression388
A novel attention based location regression network is designed to predict the temporal coordinates of sentence query from the previous attention and a multi-modal co-attention mechanism is introduced to generate not only video attention which reflects the global video structure, but also sentence attention which highlights the crucial details for temporal localization.
- Disentangled Graph Convolutional Networks387
This paper introduces the disentangled graph convolutional network (DisenGCN) to learn disentangled node representations and proposes a novel neighborhood routing mechanism, which is capable of dynamically identifying the latent factor that may have caused the edge between a node and one of its neighbors and accordingly assigning the neighbor to a channel that extracts and convolutes features to that factor.
- VTimeLLM: Empower LLM to Grasp Video Moments371
Benefits from the fine-grained temporal understanding of the videos further enable VTimeLLM to beat existing Video LLMs in video di-alogue benchmark, showing its superior cross-modal understanding and reasoning abilities.
- Social contextual recommendation317
This paper investigates social recommendation on the basis of psychology and sociology studies, which exhibit two important factors: individual preference and interpersonal influence, and proposes a novel probabilistic matrix factorization method to fuse them in latent spaces.
- Semantic Conditioned Dynamic Modulation for Temporal Sentence Grounding in Videos303
A novel semantic conditioned dynamic modulation mechanism, which leverages the sentence semantics to modulate the temporal convolution operations for better correlating and composing the sentence-relevant video contents over time.
- BeautyGAN283
A dual input/output Generative Adversarial Network that enables the network to learn translation on instance-level through unsupervised adversarial learning, and could generate visually pleasant makeup faces and accurate transferring results.
- Disentangled Self-Supervision in Sequential Recommenders271
This paper proposes a sequence-to-sequence (seq2seq) training strategy based on latent self-supervision and disentanglement, which performs self- supervision in the latent space, instead of reconstructing the items in the future sequence individually.
- 268
- Disentangled Representation Learning260
This article comprehensively investigates DRL from various aspects including motivations, definitions, methodologies, evaluations, applications, and model designs, and points out challenges in DRL as well as potential research directions deserving future investigations.
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- A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions247
A novel taxonomy of deep clustering is introduced based on how two fundamental components: the representation learning module and the clustering module interact, specifically through multistage, generative, iterative, and simultaneous approaches.
- Structural Deep Embedding for Hyper-Networks216
It is theoretically prove that any linear similarity metric in embedding space commonly used in existing methods cannot maintain the indecomposibility property in hyper-networks, and thus a new deep model is proposed to realize a non-linear tuplewise similarity function while preserving both local and global proximities in the formedembedding space.
- Arbitrary-Order Proximity Preserved Network Embedding210
The eigen-decomposition reweighting theorem is theoretically proved, revealing the intrinsic relationship between proximities of different orders and proposed AROPE (arbitrary-order proximity preserved embedding), a novel network embedding method based on SVD framework.
- AVQA: A Dataset for Audio-Visual Question Answering on Videos199
This paper introduces AVQA, a new audio-visual question answering dataset on videos in real-life scenarios and proposes a Hierarchical Audio-Visual Fusing module to model multiple semantic correlations among audio, visual, and text modalities and conduct ablation studies to analyze the role of different modalities on the authors' datasets.
- Modelling and performance analysis of the distributed scheduler in IEEE 802.16 mesh mode188
This work develops a stochastic model for the distributed scheduler of the mesh mode of the IEEE 802.16 protocol and analyzes the scheduler performance under various conditions, and the analytical results match very well with the ns-2 simulation results.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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