22,194 research outputs found
Mining and Analyzing the Academic Network
Social Network research has attracted the interests of many researchers, not only in analyzing the online social networking applications, such as Facebook and Twitter, but also in providing comprehensive services in scientific research domain. We define an Academic Network as a social network which integrates scientific factors, such as authors, papers, affiliations, publishing venues, and their relationships, such as co-authorship among authors and citations among papers. By mining and analyzing the academic network, we can provide users comprehensive services as searching for research experts, published papers, conferences, as well as detecting research communities or the evolutions hot research topics. We can also provide recommendations to users on with whom to collaborate, whom to cite and where to submit.In this dissertation, we investigate two main tasks that have fundamental applications in the academic network research. In the first, we address the problem of expertise retrieval, also known as expert finding or ranking, in which we identify and return a ranked list of researchers, based upon their estimated expertise or reputation, to user-specified queries. In the second, we address the problem of research action recommendation (prediction), specifically, the tasks of publishing venue recommendation, citation recommendation and coauthor recommendation. For both tasks, to effectively mine and integrate heterogeneous information and therefore develop well-functioning ranking or recommender systems is our principal goal. For the task of expertise retrieval, we first proposed or applied three modified versions of PageRank-like algorithms into citation network analysis; we then proposed an enhanced author-topic model by simultaneously modeling citation and publishing venue information; we finally incorporated the pair-wise learning-to-rank algorithm into traditional topic modeling process, and further improved the model by integrating groups of author-specific features. For the task of research action recommendation, we first proposed an improved neighborhood-based collaborative filtering approach for publishing venue recommendation; we then applied our proposed enhanced author-topic model and demonstrated its effectiveness in both cited author prediction and publishing venue prediction; finally we proposed an extended latent factor model that can jointly model several relations in an academic environment in a unified way and verified its performance in four recommendation tasks: the recommendation on author-co-authorship, author-paper citation, paper-paper citation and paper-venue submission. Extensive experiments conducted on large-scale real-world data sets demonstrated the superiority of our proposed models over other existing state-of-the-art methods
Communities, Knowledge Creation, and Information Diffusion
In this paper, we examine how patterns of scientific collaboration contribute
to knowledge creation. Recent studies have shown that scientists can benefit
from their position within collaborative networks by being able to receive more
information of better quality in a timely fashion, and by presiding over
communication between collaborators. Here we focus on the tendency of
scientists to cluster into tightly-knit communities, and discuss the
implications of this tendency for scientific performance. We begin by reviewing
a new method for finding communities, and we then assess its benefits in terms
of computation time and accuracy. While communities often serve as a taxonomic
scheme to map knowledge domains, they also affect how successfully scientists
engage in the creation of new knowledge. By drawing on the longstanding debate
on the relative benefits of social cohesion and brokerage, we discuss the
conditions that facilitate collaborations among scientists within or across
communities. We show that successful scientific production occurs within
communities when scientists have cohesive collaborations with others from the
same knowledge domain, and across communities when scientists intermediate
among otherwise disconnected collaborators from different knowledge domains. We
also discuss the implications of communities for information diffusion, and
show how traditional epidemiological approaches need to be refined to take
knowledge heterogeneity into account and preserve the system's ability to
promote creative processes of novel recombinations of idea
NodeTrix: Hybrid Representation for Analyzing Social Networks
The need to visualize large social networks is growing as hardware
capabilities make analyzing large networks feasible and many new data sets
become available. Unfortunately, the visualizations in existing systems do not
satisfactorily answer the basic dilemma of being readable both for the global
structure of the network and also for detailed analysis of local communities.
To address this problem, we present NodeTrix, a hybrid representation for
networks that combines the advantages of two traditional representations:
node-link diagrams are used to show the global structure of a network, while
arbitrary portions of the network can be shown as adjacency matrices to better
support the analysis of communities. A key contribution is a set of interaction
techniques. These allow analysts to create a NodeTrix visualization by dragging
selections from either a node-link or a matrix, flexibly manipulate the
NodeTrix representation to explore the dataset, and create meaningful summary
visualizations of their findings. Finally, we present a case study applying
NodeTrix to the analysis of the InfoVis 2004 coauthorship dataset to illustrate
the capabilities of NodeTrix as both an exploration tool and an effective means
of communicating results
Solutions to Detect and Analyze Online Radicalization : A Survey
Online Radicalization (also called Cyber-Terrorism or Extremism or
Cyber-Racism or Cyber- Hate) is widespread and has become a major and growing
concern to the society, governments and law enforcement agencies around the
world. Research shows that various platforms on the Internet (low barrier to
publish content, allows anonymity, provides exposure to millions of users and a
potential of a very quick and widespread diffusion of message) such as YouTube
(a popular video sharing website), Twitter (an online micro-blogging service),
Facebook (a popular social networking website), online discussion forums and
blogosphere are being misused for malicious intent. Such platforms are being
used to form hate groups, racist communities, spread extremist agenda, incite
anger or violence, promote radicalization, recruit members and create virtual
organi- zations and communities. Automatic detection of online radicalization
is a technically challenging problem because of the vast amount of the data,
unstructured and noisy user-generated content, dynamically changing content and
adversary behavior. There are several solutions proposed in the literature
aiming to combat and counter cyber-hate and cyber-extremism. In this survey, we
review solutions to detect and analyze online radicalization. We review 40
papers published at 12 venues from June 2003 to November 2011. We present a
novel classification scheme to classify these papers. We analyze these
techniques, perform trend analysis, discuss limitations of existing techniques
and find out research gaps
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