• The global and local structure are jointly considered for deep graph learning networks. relationship of the structures mined by hierarchical progressive learning. different fusion dynamically encoded their interdependence. Graph (GL) can capture distribution (graph structure) data based on convolutional networks (GCN), quality directly influences GCN semi-supervised classification. Most exist...

Exploiting global factors and embedding them directly into local graphs in point clouds are challenging due to dense points irregular structure. To accomplish this goal, we propose a novel end-to-end trainable graph attention network that extracts features terms of graphs. Our presents general graph, which obtains the most fundamental based on order positions different neighborhoods. Central is...

In this paper, we consider edge colorings of the complete graph, where the vertices are points in R, and each color class Ei is defined by a semi-algebraic relation of constant complexity on the point set. One of our main results is a multicolor regularity lemma: For any 0 < ε < 1, the vertex set of any such edge colored complete graph with m colors can be equitably partitioned into at most (m/...

For a nontrivial connected graph G with no isolated vertex, nonempty subset D \(\subseteq\) V (G) is rings dominating set if and for each vertex \(\upsilon\) \(\in\) \ adjacent to at least two vertices in D. Thus, the of all D, \(\mid\)N(\(\upsilon\)) \(\cap\) (V D)\(\mid\) \(\ge\) 2. Moreover, called minimum smallest size given graph. The cardinality domination number G, denoted by \(\gamma\)r...

For a set of n points in the plane, this paper presents simple kinetic data structures (KDS’s) for solutions to some fundamental proximity problems, namely, the all nearest neighbors problem, the closest pair problem, and the Euclidean minimum spanning tree (EMST) problem. Also, the paper introduces KDS’s for maintenance of two well-studied sparse proximity graphs, the Yao graph and the Semi-Ya...

In a traditional machine learning task, the goal is training a classifier using only labeled data (data feature/label pairs) in order to be able to generalize on completely new data to be labeled by the classifier. Unluckily in many cases it is difficult, expensive or time consuming to obtain the labeled instances needed for training, also because we usually require a human supervisor to annota...