13,785 research outputs found
On Graph Stream Clustering with Side Information
Graph clustering becomes an important problem due to emerging applications
involving the web, social networks and bio-informatics. Recently, many such
applications generate data in the form of streams. Clustering massive, dynamic
graph streams is significantly challenging because of the complex structures of
graphs and computational difficulties of continuous data. Meanwhile, a large
volume of side information is associated with graphs, which can be of various
types. The examples include the properties of users in social network
activities, the meta attributes associated with web click graph streams and the
location information in mobile communication networks. Such attributes contain
extremely useful information and has the potential to improve the clustering
process, but are neglected by most recent graph stream mining techniques. In
this paper, we define a unified distance measure on both link structures and
side attributes for clustering. In addition, we propose a novel optimization
framework DMO, which can dynamically optimize the distance metric and make it
adapt to the newly received stream data. We further introduce a carefully
designed statistics SGS(C) which consume constant storage spaces with the
progression of streams. We demonstrate that the statistics maintained are
sufficient for the clustering process as well as the distance optimization and
can be scalable to massive graphs with side attributes. We will present
experiment results to show the advantages of the approach in graph stream
clustering with both links and side information over the baselines.Comment: Full version of SIAM SDM 2013 pape
Graph Sample and Hold: A Framework for Big-Graph Analytics
Sampling is a standard approach in big-graph analytics; the goal is to
efficiently estimate the graph properties by consulting a sample of the whole
population. A perfect sample is assumed to mirror every property of the whole
population. Unfortunately, such a perfect sample is hard to collect in complex
populations such as graphs (e.g. web graphs, social networks etc), where an
underlying network connects the units of the population. Therefore, a good
sample will be representative in the sense that graph properties of interest
can be estimated with a known degree of accuracy. While previous work focused
particularly on sampling schemes used to estimate certain graph properties
(e.g. triangle count), much less is known for the case when we need to estimate
various graph properties with the same sampling scheme. In this paper, we
propose a generic stream sampling framework for big-graph analytics, called
Graph Sample and Hold (gSH). To begin, the proposed framework samples from
massive graphs sequentially in a single pass, one edge at a time, while
maintaining a small state. We then show how to produce unbiased estimators for
various graph properties from the sample. Given that the graph analysis
algorithms will run on a sample instead of the whole population, the runtime
complexity of these algorithm is kept under control. Moreover, given that the
estimators of graph properties are unbiased, the approximation error is kept
under control. Finally, we show the performance of the proposed framework (gSH)
on various types of graphs, such as social graphs, among others
Analyzing Massive Graphs in the Semi-streaming Model
Massive graphs arise in a many scenarios, for example,
traffic data analysis in large networks, large scale scientific
experiments, and clustering of large data sets.
The semi-streaming model was proposed for processing massive graphs. In the semi-streaming model, we have a random
accessible memory which is near-linear in the number of vertices.
The input graph (or equivalently, edges in the graph)
is presented as a sequential list of edges (insertion-only model)
or edge insertions and deletions (dynamic model). The list
is read-only but we may make multiple passes over the list.
There has been a few results in the insertion-only model
such as computing distance spanners and approximating
the maximum matching.
In this thesis, we present some algorithms and techniques
for (i) solving more complex problems in the semi-streaming model,
(for example, problems in the dynamic model) and (ii) having
better solutions for the problems which have been studied
(for example, the maximum matching problem). In course of both
of these, we develop new techniques with broad applications and
explore the rich trade-offs between the complexity of models
(insertion-only streams vs. dynamic streams), the number
of passes, space, accuracy, and running time.
1. We initiate the study of dynamic graph streams.
We start with basic problems such as the connectivity
problem and computing the minimum spanning tree.
These problems are
trivial in the insertion-only model. However, they require
non-trivial (and multiple passes for computing the exact minimum
spanning tree) algorithms in the
dynamic model.
2. Second, we present a graph sparsification algorithm in the
semi-streaming model. A graph sparsification
is a sparse graph that approximately preserves
all the cut values of a graph.
Such a graph acts as an oracle for solving cut-related problems,
for example, the minimum cut problem and the multicut problem.
Our algorithm produce a graph sparsification with high probability
in one pass.
3. Third, we use the primal-dual algorithms
to develop the semi-streaming algorithms.
The primal-dual algorithms have been widely accepted
as a framework for solving linear programs
and semidefinite programs faster.
In contrast, we apply the method for reducing space and
number of passes in addition to reducing the running time.
We also present some examples that arise in applications
and show how to apply the techniques:
the multicut problem, the correlation clustering problem,
and the maximum matching problem. As a consequence,
we also develop near-linear time algorithms for the -matching
problems which were not known before
Network Sampling: From Static to Streaming Graphs
Network sampling is integral to the analysis of social, information, and
biological networks. Since many real-world networks are massive in size,
continuously evolving, and/or distributed in nature, the network structure is
often sampled in order to facilitate study. For these reasons, a more thorough
and complete understanding of network sampling is critical to support the field
of network science. In this paper, we outline a framework for the general
problem of network sampling, by highlighting the different objectives,
population and units of interest, and classes of network sampling methods. In
addition, we propose a spectrum of computational models for network sampling
methods, ranging from the traditionally studied model based on the assumption
of a static domain to a more challenging model that is appropriate for
streaming domains. We design a family of sampling methods based on the concept
of graph induction that generalize across the full spectrum of computational
models (from static to streaming) while efficiently preserving many of the
topological properties of the input graphs. Furthermore, we demonstrate how
traditional static sampling algorithms can be modified for graph streams for
each of the three main classes of sampling methods: node, edge, and
topology-based sampling. Our experimental results indicate that our proposed
family of sampling methods more accurately preserves the underlying properties
of the graph for both static and streaming graphs. Finally, we study the impact
of network sampling algorithms on the parameter estimation and performance
evaluation of relational classification algorithms
Growing Story Forest Online from Massive Breaking News
We describe our experience of implementing a news content organization system
at Tencent that discovers events from vast streams of breaking news and evolves
news story structures in an online fashion. Our real-world system has distinct
requirements in contrast to previous studies on topic detection and tracking
(TDT) and event timeline or graph generation, in that we 1) need to accurately
and quickly extract distinguishable events from massive streams of long text
documents that cover diverse topics and contain highly redundant information,
and 2) must develop the structures of event stories in an online manner,
without repeatedly restructuring previously formed stories, in order to
guarantee a consistent user viewing experience. In solving these challenges, we
propose Story Forest, a set of online schemes that automatically clusters
streaming documents into events, while connecting related events in growing
trees to tell evolving stories. We conducted extensive evaluation based on 60
GB of real-world Chinese news data, although our ideas are not
language-dependent and can easily be extended to other languages, through
detailed pilot user experience studies. The results demonstrate the superior
capability of Story Forest to accurately identify events and organize news text
into a logical structure that is appealing to human readers, compared to
multiple existing algorithm frameworks.Comment: Accepted by CIKM 2017, 9 page
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