Graph embedding learns low-dimensional representations for nodes or edges on the graph, which is widely applied in many real-world applications. Excessive graph mining promotes research of attack methods embedding. Most generate perturbations that maximize deviation prediction confidence. They are difficult to accurately misclassify instances into target label, and nonminimized more easily dete...

The fastest deterministic algorithms for connected components take logarithmic time and perform superlinear work on a Parallel Random Access Machine (PRAM). These maintain spanning forest by merging compressing trees, which requires pointer-chasing operations that increase memory access latency are limited to shared-memory systems. Many of these PRAM also very complicated implement. Another pop...

Annotating remote sensing images is a challenging task for its labor demanding annotation process and requirement of expert knowledge, especially when images can be annotated with multiple semantic concepts (or labels). To automatically annotate these multi-label images, we introduce an approach called Multi-Label Classification based on Low Rank Representation (MLC-LRR). MLC-LRR firstly utiliz...

With the recent advances in graph neural networks, there is a rising number of studies on graph-based multi-label classification with consideration object dependencies within visual data. Nevertheless, representations can become indistinguishable due to complex nature label relationships. We propose image framework based transformer networks fully exploit inter-label interactions. The paper pre...

A vertex irregular total k-labeling of a graph G with vertex set V and edge set E is an assignment of positive integer labels {1, 2, ..., k} to both vertices and edges so that the weights calculated at vertices are distinct. The total vertex irregularity strength of G, denoted by tvs(G)is the minimum value of the largest label k over all such irregular assignment. In this paper, we study the to...

Graph-based semi-supervised learning methods are based on some smoothness assumption about the data. As a discrete approximation of the data manifold, the graph plays a crucial role in the success of such graphbased methods. In most existing methods, graph construction makes use of a predefined weighting function without utilizing label information even when it is available. In this work, by in...

We study the problem of predicting the labelling of a graph. The graph is given and a trial sequence of (vertex,label) pairs is then incrementally revealed to the learner. On each trial a vertex is queried and the learner predicts a boolean label. The true label is then returned. The learner’s goal is to minimise mistaken predictions. We propose minimum p-seminorm interpolation to solve this pr...

In multi-label learning problems, the class labels are correlated and label correlations can be leveraged to improve predictive performance of a classifier. Methods that consider high-order in space, usually do not utilize pairwise correlations. most these methods, considered as prior knowledge, which misleading problems with noisy or missing labels. such cases, correlation part model training ...

We propose a method for domain adaptation on graphs. Given sufficiently many observations of the label function on a source graph, we study the problem of transferring the label information from the source graph to a target graph for estimating the target label function. Our assumption about the relation between the two domains is that the frequency content of the label function, regarded as a ...