681,491 research outputs found
Multi-duplicated Characterization of Graph Structures using Information Gain Ratio for Graph Neural Networks
Various graph neural networks (GNNs) have been proposed to solve node
classification tasks in machine learning for graph data. GNNs use the
structural information of graph data by aggregating the features of neighboring
nodes. However, they fail to directly characterize and leverage the structural
information. In this paper, we propose multi-duplicated characterization of
graph structures using information gain ratio (IGR) for GNNs (MSI-GNN), which
enhances the performance of node classification by using an i-hop adjacency
matrix as the structural information of the graph data. In MSI-GNN, the i-hop
adjacency matrix is adaptively adjusted by two methods: (i) structural features
in the matrix are selected based on the IGR, and (ii) the selected features in
(i) for each node are duplicated and combined flexibly. In an experiment, we
show that our MSI-GNN outperforms GCN, H2GCN, and GCNII in terms of average
accuracies in benchmark graph datasets.Comment: 20pages, 8 figure
Context-Aware Recursive Bayesian Graph Traversal in BCIs
Noninvasive brain computer interfaces (BCI), and more specifically
Electroencephalography (EEG) based systems for intent detection need to
compensate for the low signal to noise ratio of EEG signals. In many
applications, the temporal dependency information from consecutive decisions
and contextual data can be used to provide a prior probability for the upcoming
decision. In this study we proposed two probabilistic graphical models (PGMs),
using context information and previously observed EEG evidences to estimate a
probability distribution over the decision space in graph based decision-making
mechanism. In this approach, user moves a pointer to the desired vertex in the
graph in which each vertex represents an action. To select a vertex, a Select
command, or a proposed probabilistic Selection criterion (PSC) can be used to
automatically detect the user intended vertex. Performance of different PGMs
and Selection criteria combinations are compared over a keyboard based on a
graph layout. Based on the simulation results, probabilistic Selection
criterion along with the probabilistic graphical model provides the highest
performance boost for individuals with pour calibration performance and
achieving the same performance for individuals with high calibration
performance.Comment: This work has been submitted to EMBC 201
Multicommodity Multicast, Wireless and Fast
We study rumor spreading in graphs, specifically multicommodity multicast problem under the wireless model: given source-destination pairs in the graph, one needs to find the fastest schedule to transfer information from each source to the corresponding destination. Under the wireless model, nodes can transmit to any subset of their neighbors in synchronous time steps, as long as they either transmit or receive from at most one transmitter during the same time step. We improve approximation ratio for this problem from O~(n^(2/3)) to O~(n^((1/2) + epsilon)) on n-node graphs. We also design an algorithm that satisfies p given demand pairs in O(OPT + p) steps, where OPT is the length of an optimal schedule, by reducing it to the well-studied packet routing problem. In the case where underlying graph is an n-node tree, we improve the previously best-known approximation ratio of O((log n)/(log log n)) to 3. One consequence of our proof is a simple constructive rule for optimal broadcasting in a tree under a widely studied telephone model
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