Network virtualization has enabled new business models by allowing infrastructure providers to lease or share their physical network. To concurrently run multiple customized virtual network services, such infrastructure providers need to run a virtual network embedding protocol. The virtual network embedding is the (NP-hard) problem of matching constrained virtual networks onto the physical net...

Concurrent with numerous theoretical results on metric embeddings, a growing body of research in the networking community has studied the distance matrix defined by node-to-node latencies in the Internet, resulting in a number of recent approaches that approximately embed this distance matrix into low-dimensional Euclidean space. A fundamental distinction between the theoretical approaches to e...

This paper provides a linking theory between the minimalist grammar formalism and off-line behavioural data. We examine the transient stack states of a top-down parser for Minimalist Grammars as it analyzes embedded sentences in English, Dutch and German. We find that the number of time steps that a derivation tree node persist on the parser’s stack derives the observed contrasts in English cen...

One challenge for sentiment analysis as well as many other natural language processing task is the language variation. In this project, based on the intuition of language homophily, we aim to solve this problem by combing social network information. We propose a new model that measure the interaction between author embedding and sentence words’ embeddings. This model is based on the current sta...

We study three different kinds of embeddings of tree patterns: weakly-injective, ancestor-preserving, and lca-preserving. While each of them is often referred to as injective embedding, they form a proper hierarchy and their computational properties vary (from P to NP-complete). We present a thorough study of the complexity of the model checking problem, i.e., is there an embedding of a given t...

The problem of dynamic tree embedding in static networks is studied in this paper. We provide a unified framework for studying the performance of randomized tree embedding algorithms which allow a newly created tree node to take a random walk of a short distance to reach a processor nearby. In particular, we propose simple randomized algorithms on several most common and important static networ...

We investigate an unsupervised generative approach for network embedding. A multi-task Siamese neural network structure is formulated to connect embedding vectors and our objective to preserve the global node ranking and local proximity of nodes. We provide deeper analysis to connect the proposed proximity objective to link prediction and community detection in the network. We show our model ca...

Existing distributed representations are limited in utilizing structured knowledge to improve semantic relatedness modeling. We propose a principled framework of embedding entities that integrates hierarchical information from large-scale knowledge bases. The novel embedding model associates each category node of the hierarchy with a distance metric. To capture structured semantics, the entity ...

Localization is the process of finding the location coordinates of a node. Distances from nodes with known coordinates is required for this computation. In most of the literature, the errors in these distance measurements are assumed to be independent. However, in the real world this does not hold true. There is a need to design algorithms for the case when the errors are correlated. In this wo...

{ The problem of dynamic tree embedding in static networks is studied in this paper. We provide a uniied framework for studying the performance of randomized tree embedding algorithms which allow a newly created tree node to take a random walk of a short distance to reach a processor nearby. In particular, we propose simple randomized algorithms on several most common and important static netwo...