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Found 17 papers, 1 papers with code
SPP-CNN: An Efficient Framework for Network Robustness Prediction
This paper addresses the robustness of a network to sustain its connectivity and controllability against malicious attacks.
Steering Graph Neural Networks with Pinning Control
In the semi-supervised setting where labeled data are largely limited, it remains to be a big challenge for message passing based graph neural networks (GNNs) to learn feature representations for the nodes with the same class label that is distributed discontinuously over the graph.
Stochastic Event-triggered Variational Bayesian Filtering
This paper proposes an event-triggered variational Bayesian filter for remote state estimation with unknown and time-varying noise covariances.
A Learning Convolutional Neural Network Approach for Network Robustness Prediction
Extensive experimental studies on both synthetic and real-world networks, both directed and undirected, demonstrate that 1) the proposed LFR-CNN performs better than other two state-of-the-art prediction methods, with significantly lower prediction errors; 2) LFR-CNN is insensitive to the variation of the network size, which significantly extends its applicability; 3) although LFR-CNN needs more time to perform feature learning, it can achieve accurate prediction faster than attack simulations; 4) LFR-CNN not only can accurately predict network robustness, but also provides a good indicator for connectivity robustness, better than the classical spectral measures.
Sensor Scheduling Design for Complex Networks under a Distributed State Estimation Framework
The goal of this paper is to seek an optimal sensor scheduling policy minimizing the overall estimation errors.
Consensus-Based Distributed Filtering with Fusion Step Analysis
For consensus on measurement-based distributed filtering (CMDF), through infinite consensus fusion operations during each sampling interval, each node in the sensor network can achieve optimal filtering performance with centralized filtering.
Topological and Algebraic Structures of Atanassov's Intuitionistic Fuzzy-Values Space
By introducing a new operator for IFVs via the linear order based on a score function and an accuracy function, we show that such an operator is a strong negation on IFVs.
Hyperparameter-free and Explainable Whole Graph Embedding
Graphs can be used to describe complex systems.
Computing Cliques and Cavities in Networks
Complex networks contain complete subgraphs such as nodes, edges, triangles, etc., referred to as simplices and cliques of different orders.
M-Evolve: Structural-Mapping-Based Data Augmentation for Graph Classification
Graph classification, which aims to identify the category labels of graphs, plays a significant role in drug classification, toxicity detection, protein analysis etc.
Adversarial Attacks to Scale-Free Networks: Testing the Robustness of Physical Criteria
It is found that both DALR and DILR are more effective than RLR, in the sense that rewiring a smaller number of links can succeed in the same attack.
Social and Information Networks Physics and Society
Predicting Network Controllability Robustness: A Convolutional Neural Network Approach
Under the new framework, a fairly large number of training data generated by simulations are used to train a convolutional neural network for predicting the controllability robustness according to the input network-adjacency matrices, without performing conventional attack simulations.
Subgraph Networks with Application to Structural Feature Space Expansion
In other words, the structural features of SGNs can complement that of the original network for better network classification, regardless of the feature extraction method used, such as the handcrafted, network embedding and kernel-based methods.
Ranked #10 on
Graph Classification
on MUTAG
On-line Search History-assisted Restart Strategy for Covariance Matrix Adaptation Evolution Strategy
The entire on-line search history of cNrGA is stored in a binary space partitioning (BSP) tree, which is effective for performing local search.
Target Defense Against Link-Prediction-Based Attacks via Evolutionary Perturbations
This is the first time to study privacy protection on targeted links against link-prediction-based attacks.
Social and Information Networks Physics and Society
Local communities obstruct global consensus: Naming game on multi-local-world networks
Naming game, on the other hand, is a social communication model that simulates the process of learning a name of an object within a community of humans, where the individuals can generally reach global consensus asymptotically through iterative pair-wise conversations.
Communicating with sentences: A multi-word naming game model
Naming game simulates the process of naming an object by a single word, in which a population of communicating agents can reach global consensus asymptotically through iteratively pair-wise conversations.
