Search Results for author:
Found 13 papers, 6 papers with code
Control-based Graph Embeddings with Data Augmentation for Contrastive Learning
In this paper, we study the problem of unsupervised graph representation learning by harnessing the control properties of dynamical networks defined on graphs.
Enhanced Graph Neural Networks with Ego-Centric Spectral Subgraph Embeddings Augmentation
The superior performance of GNNs often correlates with the availability and quality of node-level features in the input networks.
Controllability Backbone in Networks
Thus, we utilize two lower bounds on the network's SSC based on the zero forcing notion and graph distances.
A Survey of Graph Unlearning
By laying a solid foundation and fostering continued progress, this survey seeks to inspire researchers to further advance the field of graph unlearning, thereby instilling confidence in the ethical growth of AI systems and reinforcing the responsible application of machine learning techniques in various domains.
NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics
We delve deeply into the dataset generation search space by crafting 35 datasets that encompass static and dynamic brain connectivity, running in excess of 15 baseline methods for benchmarking.
Learning-Based Heuristic for Combinatorial Optimization of the Minimum Dominating Set Problem using Graph Convolutional Networks
The minimum dominating set problem seeks to find a dominating set of minimum cardinality and is a well-established NP-hard combinatorial optimization problem.
Sequential Graph Neural Networks for Source Code Vulnerability Identification
However, this task is rather challenging owing to the absence of reliable and adequately managed datasets and learning models.
Computing Graph Descriptors on Edge Streams
Operating on edge streams allows us to avoid storing the entire graph in memory, and controlling the sample size enables us to keep the runtime of our algorithms within desired bounds.
Edge Augmentation with Controllability Constraints in Directed Laplacian Networks
In this paper, we study the maximum edge augmentation problem in directed Laplacian networks to improve their robustness while preserving lower bounds on their strong structural controllability (SSC).
Resilient Distributed Vector Consensus Using Centerpoints
In this paper, we study the resilient vector consensus problem in networks with adversarial agents and improve resilience guarantees of existing algorithms.
Estimating Descriptors for Large Graphs
State-of-the-art algorithms for computing descriptors require the entire graph to be in memory, entailing a huge memory footprint, and thus do not scale well to increasing sizes of real-world networks.
Databases
Computation of the Distance-based Bound on Strong Structural Controllability in Networks
The bound is based on a sequence of vectors containing the distances between leaders (nodes with external inputs) and followers (remaining nodes) in the underlying network graph.
Efficient Approximation Algorithms for Strings Kernel Based Sequence Classification
Sequence classification algorithms, such as SVM, require a definition of distance (similarity) measure between two sequences.
