A visibility representation of a graph G is to represent the nodes of G with non-overlapping horizontal line segments such that the line segments representing any two distinct adjacent nodes are vertically visible to each other. If G is a plane graph, i.e., a planar graph equipped with a planar embedding, a visibility representation of G has the additional requirement of reflecting the given pl...

Recently, Network Embedding (NE) has become one of the most attractive research topics in machine learning and data mining. NE approaches have achieved promising performance various graph mining tasks including link prediction node clustering classification. A wide variety methods focus on proximity networks, they learn community-oriented embedding for each node, where corresponding representat...

Role is a fundamental concept in the analysis of behavior and function interacting entities complex networks. discovery task uncovering hidden roles nodes within network. Node are commonly defined terms equivalence classes. Two have same role if they fall class. Automorphic equivalence, where two equivalent when can swap their labels to form an isomorphic graph, captures this notion role. The b...

We present a new method for explicit graph embedding. Our algorithm extracts a feature vector for an undirected attributed graph. The proposed feature vector encodes details about the number of nodes, number of edges, node degrees, the attributes of nodes and the attributes of edges in the graph. The first two features are for the number of nodes and the number of edges. These are followed by w...

Learning distributed representations for nodes in graphs has become an important problem that underpins a wide spectrum of applications. Existing methods to this problem learn representations by optimizing a softmax objective while constraining the dimension of embedding vectors. We argue that the generalization performance of these methods are probably not due to the dimensionality constraint ...

This paper establishes a framework that hierarchically integrates symbolic and sub-symbolic representations in an architecture for cognitive robotics. It is formalised abstractly as nodes in a hierarchy, with each node a sub-task that maintains its own belief-state and generates behaviour. An instantiation is developed for a real robot building towers of blocks, subject to human interference; t...