Sensor networks are emerging as a paradigm for future computing, but pose a number of challenges in the fields of networking and distributed computation. One challenge is to devise a greedy routing protocol—one that routes messages through the network using only information available at a node or its neighbors. Modeling the connectivity graph of a sensor network as a 3-connected planar graph, w...

The incrementally extensible hypercube (IEH) graph is a generalization of binary hypercube networks in that the number of nodes can be arbitrary in contrast to a strict power of 2. In this paper, the authors present an efficient model to fulfill the embedding of a full binary tree into a full IEH graph. As the model the authors proposed, an algorithm is developed for the embedding with expansio...

We present a novel graph embedding to speed-up distance-range and k-nearest neighbor queries on static and/or dynamic objects located on a (weighted) graph that is applicable also for very large networks. Our method extends an existing embedding called reference node embedding which can be used to compute accurate lower and upper bounding filters for the true shortest path distance. In order to...

AbstructWe consider the problem of embedding a cubeconnected cycles graph (CCC) into a hypercube with edge faults. Our main result is an algorithm that, given a l i t of faulty edges, computes an embedding of the CCC that spans all of the nodes and avoids all of the faulty edges. The algorithm has optimal running time and tolerates the maximum number of faults (in a worst-case setting). Because...

Abstract Graph representation learning methods opened new avenues for addressing complex, real-world problems represented by graphs. However, many graphs used in these applications comprise millions of nodes and billions edges are beyond the capabilities current software implementations. We present GRAPE (Graph Representation Learning, Prediction Evaluation), a resource graph processing embeddi...

This paper proposes a Metapath-Infused Exponential Decay graph neural network (MIED) approach for node embedding in heterogeneous graphs. It is designed to address limitations existing methods, which usually lose the information during feature alignment and ignore different importance of nodes metapath aggregation. Firstly, convolutional (GCN) applied on subgraphs, derived from original with gi...

This paper addresses social network embedding, which aims to embed social network nodes, including user profile information, into a latent lowdimensional space. Most of the existing works on network embedding only consider network structure, but ignore user-generated content that could be potentially helpful in learning a better joint network representation. Different from rich node content in ...

Analysis of large graphs is critical to the ongoing growth of search engines and social networks. One class of queries centers around node affinity, often quantified by random-walk distances between node pairs, including hitting time, commute time, and personalized PageRank (PPR). Despite the potential of these “metrics,” they are rarely, if ever, used in practice, largely due to extremely high...

Network embedding has gained more attentions in recent years. It has been shown that the learned lowdimensional node vector representations could advance a myriad of graph mining tasks such as node classification, community detection, and link prediction. A vast majority of the existing efforts are overwhelmingly devoted to single-layered networks or homogeneous networks with a single type of n...