321,362 research outputs found
Dynamic Discovery of Type Classes and Relations in Semantic Web Data
The continuing development of Semantic Web technologies and the increasing
user adoption in the recent years have accelerated the progress incorporating
explicit semantics with data on the Web. With the rapidly growing RDF (Resource
Description Framework) data on the Semantic Web, processing large semantic
graph data have become more challenging. Constructing a summary graph structure
from the raw RDF can help obtain semantic type relations and reduce the
computational complexity for graph processing purposes. In this paper, we
addressed the problem of graph summarization in RDF graphs, and we proposed an
approach for building summary graph structures automatically from RDF graph
data. Moreover, we introduced a measure to help discover optimum class
dissimilarity thresholds and an effective method to discover the type classes
automatically. In future work, we plan to investigate further improvement
options on the scalability of the proposed method
Towards Scalable Visual Exploration of Very Large RDF Graphs
In this paper, we outline our work on developing a disk-based infrastructure
for efficient visualization and graph exploration operations over very large
graphs. The proposed platform, called graphVizdb, is based on a novel technique
for indexing and storing the graph. Particularly, the graph layout is indexed
with a spatial data structure, i.e., an R-tree, and stored in a database. In
runtime, user operations are translated into efficient spatial operations
(i.e., window queries) in the backend.Comment: 12th Extended Semantic Web Conference (ESWC 2015
Graph Convolutional Neural Networks for Web-Scale Recommender Systems
Recent advancements in deep neural networks for graph-structured data have
led to state-of-the-art performance on recommender system benchmarks. However,
making these methods practical and scalable to web-scale recommendation tasks
with billions of items and hundreds of millions of users remains a challenge.
Here we describe a large-scale deep recommendation engine that we developed and
deployed at Pinterest. We develop a data-efficient Graph Convolutional Network
(GCN) algorithm PinSage, which combines efficient random walks and graph
convolutions to generate embeddings of nodes (i.e., items) that incorporate
both graph structure as well as node feature information. Compared to prior GCN
approaches, we develop a novel method based on highly efficient random walks to
structure the convolutions and design a novel training strategy that relies on
harder-and-harder training examples to improve robustness and convergence of
the model. We also develop an efficient MapReduce model inference algorithm to
generate embeddings using a trained model. We deploy PinSage at Pinterest and
train it on 7.5 billion examples on a graph with 3 billion nodes representing
pins and boards, and 18 billion edges. According to offline metrics, user
studies and A/B tests, PinSage generates higher-quality recommendations than
comparable deep learning and graph-based alternatives. To our knowledge, this
is the largest application of deep graph embeddings to date and paves the way
for a new generation of web-scale recommender systems based on graph
convolutional architectures.Comment: KDD 201
Exploring Complex Graphs by Random Walks
We present an algorithm to grow a graph with scale-free structure of {\it
in-} and {\it out-links} and variable wiring diagram in the class of the
world-wide Web. We then explore the graph by intentional random walks using
local next-near-neighbor search algorithm to navigate through the graph. The
topological properties such as betweenness are determined by an ensemble of
independent walkers and efficiency of the search is compared on three different
graph topologies. In addition we simulate interacting random walks which are
created by given rate and navigated in parallel, representing transport with
queueing of information packets on the graph.Comment: Latex, 4 figure
The Structure of Federal eGovernment: Using hyperlinks to analyze the .gov domain
This paper uses the hyperlink structure of federal web sites within the .gov domain to answer two research questions: to what degree does the online structure of the federal government mirror its offline hierarchy, and to what degree does the .gov web graph mirror the greater WWW graph. Findings of subgraph link analysis and Krackhardt’s graph theoretical dimensions of hierarchy analysis demonstrate clear hierarchy within the .gov domain, but also suggest great discrepancies in the linking patterns of different government departments. Structural analysis suggests that the .gov web graph is indeed a fractal leaf of the greater WWW graph
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