1,053 research outputs found
Two to Five Truths in Non-Negative Matrix Factorization
In this paper, we explore the role of matrix scaling on a matrix of counts
when building a topic model using non-negative matrix factorization. We present
a scaling inspired by the normalized Laplacian (NL) for graphs that can greatly
improve the quality of a non-negative matrix factorization. The results
parallel those in the spectral graph clustering work of \cite{Priebe:2019},
where the authors proved adjacency spectral embedding (ASE) spectral clustering
was more likely to discover core-periphery partitions and Laplacian Spectral
Embedding (LSE) was more likely to discover affinity partitions. In text
analysis non-negative matrix factorization (NMF) is typically used on a matrix
of co-occurrence ``contexts'' and ``terms" counts. The matrix scaling inspired
by LSE gives significant improvement for text topic models in a variety of
datasets. We illustrate the dramatic difference a matrix scalings in NMF can
greatly improve the quality of a topic model on three datasets where human
annotation is available. Using the adjusted Rand index (ARI), a measure cluster
similarity we see an increase of 50\% for Twitter data and over 200\% for a
newsgroup dataset versus using counts, which is the analogue of ASE. For clean
data, such as those from the Document Understanding Conference, NL gives over
40\% improvement over ASE. We conclude with some analysis of this phenomenon
and some connections of this scaling with other matrix scaling methods
Name Disambiguation from link data in a collaboration graph using temporal and topological features
In a social community, multiple persons may share the same name, phone number
or some other identifying attributes. This, along with other phenomena, such as
name abbreviation, name misspelling, and human error leads to erroneous
aggregation of records of multiple persons under a single reference. Such
mistakes affect the performance of document retrieval, web search, database
integration, and more importantly, improper attribution of credit (or blame).
The task of entity disambiguation partitions the records belonging to multiple
persons with the objective that each decomposed partition is composed of
records of a unique person. Existing solutions to this task use either
biographical attributes, or auxiliary features that are collected from external
sources, such as Wikipedia. However, for many scenarios, such auxiliary
features are not available, or they are costly to obtain. Besides, the attempt
of collecting biographical or external data sustains the risk of privacy
violation. In this work, we propose a method for solving entity disambiguation
task from link information obtained from a collaboration network. Our method is
non-intrusive of privacy as it uses only the time-stamped graph topology of an
anonymized network. Experimental results on two real-life academic
collaboration networks show that the proposed method has satisfactory
performance.Comment: The short version of this paper has been accepted to ASONAM 201
Communities in Networks
We survey some of the concepts, methods, and applications of community
detection, which has become an increasingly important area of network science.
To help ease newcomers into the field, we provide a guide to available
methodology and open problems, and discuss why scientists from diverse
backgrounds are interested in these problems. As a running theme, we emphasize
the connections of community detection to problems in statistical physics and
computational optimization.Comment: survey/review article on community structure in networks; published
version is available at
http://people.maths.ox.ac.uk/~porterm/papers/comnotices.pd
Edge-Parallel Graph Encoder Embedding
New algorithms for embedding graphs have reduced the asymptotic complexity of
finding low-dimensional representations. One-Hot Graph Encoder Embedding (GEE)
uses a single, linear pass over edges and produces an embedding that converges
asymptotically to the spectral embedding. The scaling and performance benefits
of this approach have been limited by a serial implementation in an interpreted
language. We refactor GEE into a parallel program in the Ligra graph engine
that maps functions over the edges of the graph and uses lock-free atomic
instrutions to prevent data races. On a graph with 1.8B edges, this results in
a 500 times speedup over the original implementation and a 17 times speedup
over a just-in-time compiled version.Comment: 4 pages, 4 figure
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