80,422 research outputs found
A Combinatorial Necessary and Sufficient Condition for Cluster Consensus
In this technical note, cluster consensus of discrete-time linear multi-agent
systems is investigated. A set of stochastic matrices is said to
be a cluster consensus set if the system achieves cluster consensus for any
initial state and any sequence of matrices taken from . By
introducing a cluster ergodicity coefficient, we present an equivalence
relation between a range of characterization of cluster consensus set under
some mild conditions including the widely adopted inter-cluster common
influence. We obtain a combinatorial necessary and sufficient condition for a
compact set to be a cluster consensus set. This combinatorial
condition is an extension of the avoiding set condition for global consensus,
and can be easily checked by an elementary routine. As a byproduct, our result
unveils that the cluster-spanning trees condition is not only sufficient but
necessary in some sense for cluster consensus problems.Comment: 6 page
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
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