SigGAN : Adversarial Model for Learning Signed Relationships in Networks
- Computer ScienceArXiv
A GAN based model for signed networks, SigGAN, inspired by the recent success of Generative Adversarial Network (GAN) based models in several applications is proposed and compared with state-of-the-art techniques on 5 real-world datasets validates the effectiveness of SigGAN.
Structure information learning for neutral links in signed network embedding
- Computer ScienceInf. Process. Manag.
Signed network representation with novel node proximity evaluation
- Computer ScienceNeural Networks
Dual Space Graph Contrastive Learning
- Computer ScienceWWW
A novel graph contrastive learning method, namely Dual Space Graph Contrastive (DSGC) Learning is proposed, to conduct graph Contrastive learning among views generated in different spaces including the hyperbolic space and the Euclidean space to bridge the spaces and leverage advantages from both sides.
Survey of Generative Methods for Social Media Analysis
- Political ScienceArXiv
This paper presents a meta-modelling study that aims to demonstrate the efforts towards in-situ applicability of EMMARM, and the aims of this study were to provide real-time information about the response of the immune system to computer attacks.
Hyperbolic Hypergraphs for Sequential Recommendation
- Computer ScienceCIKM
A novel architecture named Hyperbolic Hypergraph representation learning method for Sequential Recommendation (H2SeqRec) is proposed with the pre-training phase to alleviate the negative impact of sparse hypergraphs and design an item enhancement module to capture dynamic social information at each timestamp to improve effectiveness.
SHOWING 1-10 OF 67 REFERENCES
Signed Network Embedding in Social Media
- Computer ScienceSDM
Experimental results on two realworld datasets of social media demonstrate the effectiveness of the proposed deep learning framework SiNE for signed network embedding that optimizes an objective function guided by social theories that provide a fundamental understanding of signed social networks.
"Bridge": Enhanced Signed Directed Network Embedding
- Computer ScienceCIKM
A novel deep learning approach to incorporating two social-psychologic theories, balance and status theories, to model both triangles and "bridge'' edges in a complementary manner is presented.
Link Prediction with Signed Latent Factors in Signed Social Networks
- Computer ScienceKDD
The proposed SLF model considers four types of relationships: positive, negative, neutral and no relationship at all, and links social relationships of different types to the comprehensive, but opposite, effects of positive and negative SLFs.
Hyperbolic Entailment Cones for Learning Hierarchical Embeddings
- Computer ScienceICML
This work presents a novel method to embed directed acyclic graphs through hierarchical relations as partial orders defined using a family of nested geodesically convex cones and proves that these entailment cones admit an optimal shape with a closed form expression both in the Euclidean and hyperbolic spaces.
SNE: Signed Network Embedding
- Computer SciencePAKDD
The signed network embedding model called SNE adopts the log-bilinear model, uses node representations of all nodes along a given path, and further incorporates two signed-type vectors to capture the positive or negative relationship of each edge along the path.
Learning node and edge embeddings for signed networks
- Computer ScienceNeurocomputing
Deep Learning for Community Detection: Progress, Challenges and Opportunities
- Computer ScienceArXiv
This article summarizes the contributions of the various frameworks, models, and algorithms in each stream in deep learning along with the current challenges that remain unsolved and the future research opportunities yet to be explored.
Fuzzy Adaptive Finite-Time Fault-Tolerant Control for Strict-Feedback Nonlinear Systems
- MathematicsIEEE Transactions on Fuzzy Systems
This article devotes to investigating the issue of fuzzy adaptive control for a class of strict-feedback nonlinear systems with nonaffine nonlinear faults by adopting the dynamic surface control technique.
A Novel Finite-Time Control for Nonstrict Feedback Saturated Nonlinear Systems With Tracking Error Constraint
- Engineering, MathematicsIEEE Transactions on Systems, Man, and Cybernetics: Systems
This article investigates the neural network-based finite-time control issue for a class of nonstrict feedback nonlinear systems, which contain unknown smooth functions, input saturation, and error…