1,318 research outputs found
CEAI: CCM based Email Authorship Identification Model
In this paper we present a model for email authorship identification (EAI) by
employing a Cluster-based Classification (CCM) technique. Traditionally,
stylometric features have been successfully employed in various authorship
analysis tasks; we extend the traditional feature-set to include some more
interesting and effective features for email authorship identification (e.g.
the last punctuation mark used in an email, the tendency of an author to use
capitalization at the start of an email, or the punctuation after a greeting or
farewell). We also included Info Gain feature selection based content features.
It is observed that the use of such features in the authorship identification
process has a positive impact on the accuracy of the authorship identification
task. We performed experiments to justify our arguments and compared the
results with other base line models. Experimental results reveal that the
proposed CCM-based email authorship identification model, along with the
proposed feature set, outperforms the state-of-the-art support vector machine
(SVM)-based models, as well as the models proposed by Iqbal et al. [1, 2]. The
proposed model attains an accuracy rate of 94% for 10 authors, 89% for 25
authors, and 81% for 50 authors, respectively on Enron dataset, while 89.5%
accuracy has been achieved on authors' constructed real email dataset. The
results on Enron dataset have been achieved on quite a large number of authors
as compared to the models proposed by Iqbal et al. [1, 2]
Virus Propagation in Multiple Profile Networks
Suppose we have a virus or one competing idea/product that propagates over a
multiple profile (e.g., social) network. Can we predict what proportion of the
network will actually get "infected" (e.g., spread the idea or buy the
competing product), when the nodes of the network appear to have different
sensitivity based on their profile? For example, if there are two profiles
and in a network and the nodes of profile
and profile are susceptible to a highly spreading
virus with probabilities and
respectively, what percentage of both profiles will actually get infected from
the virus at the end? To reverse the question, what are the necessary
conditions so that a predefined percentage of the network is infected? We
assume that nodes of different profiles can infect one another and we prove
that under realistic conditions, apart from the weak profile (great
sensitivity), the stronger profile (low sensitivity) will get infected as well.
First, we focus on cliques with the goal to provide exact theoretical results
as well as to get some intuition as to how a virus affects such a multiple
profile network. Then, we move to the theoretical analysis of arbitrary
networks. We provide bounds on certain properties of the network based on the
probabilities of infection of each node in it when it reaches the steady state.
Finally, we provide extensive experimental results that verify our theoretical
results and at the same time provide more insight on the problem
An Email Attachment is Worth a Thousand Words, or Is It?
There is an extensive body of research on Social Network Analysis (SNA) based
on the email archive. The network used in the analysis is generally extracted
either by capturing the email communication in From, To, Cc and Bcc email
header fields or by the entities contained in the email message. In the latter
case, the entities could be, for instance, the bag of words, url's, names,
phones, etc. It could also include the textual content of attachments, for
instance Microsoft Word documents, excel spreadsheets, or Adobe pdfs. The nodes
in this network represent users and entities. The edges represent communication
between users and relations to the entities. We suggest taking a different
approach to the network extraction and use attachments shared between users as
the edges. The motivation for this is two-fold. First, attachments represent
the "intimacy" manifestation of the relation's strength. Second, the
statistical analysis of private email archives that we collected and Enron
email corpus shows that the attachments contribute in average around 80-90% to
the archive's disk-space usage, which means that most of the data is presently
ignored in the SNA of email archives. Consequently, we hypothesize that this
approach might provide more insight into the social structure of the email
archive. We extract the communication and shared attachments networks from
Enron email corpus. We further analyze degree, betweenness, closeness, and
eigenvector centrality measures in both networks and review the differences and
what can be learned from them. We use nearest neighbor algorithm to generate
similarity groups for five Enron employees. The groups are consistent with
Enron's organizational chart, which validates our approach.Comment: 12 pages, 4 figures, 7 tables, IML'17, Liverpool, U
Detecting Cohesive and 2-mode Communities in Directed and Undirected Networks
Networks are a general language for representing relational information among
objects. An effective way to model, reason about, and summarize networks, is to
discover sets of nodes with common connectivity patterns. Such sets are
commonly referred to as network communities. Research on network community
detection has predominantly focused on identifying communities of densely
connected nodes in undirected networks.
In this paper we develop a novel overlapping community detection method that
scales to networks of millions of nodes and edges and advances research along
two dimensions: the connectivity structure of communities, and the use of edge
directedness for community detection. First, we extend traditional definitions
of network communities by building on the observation that nodes can be densely
interlinked in two different ways: In cohesive communities nodes link to each
other, while in 2-mode communities nodes link in a bipartite fashion, where
links predominate between the two partitions rather than inside them. Our
method successfully detects both 2-mode as well as cohesive communities, that
may also overlap or be hierarchically nested. Second, while most existing
community detection methods treat directed edges as though they were
undirected, our method accounts for edge directions and is able to identify
novel and meaningful community structures in both directed and undirected
networks, using data from social, biological, and ecological domains.Comment: Published in the proceedings of WSDM '1
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