101,511 research outputs found
Mean value of a Smarandache-Type Function
In this paper, we use analytic method to study the mean value properties of
Smarandache-Type Multiplicative Functions Km(n), and give its asymptotic formula . Finally, the convolution method is used to improve the error term
Modeling Emotion Influence from Images in Social Networks
Images become an important and prevalent way to express users' activities,
opinions and emotions. In a social network, individual emotions may be
influenced by others, in particular by close friends. We focus on understanding
how users embed emotions into the images they uploaded to the social websites
and how social influence plays a role in changing users' emotions. We first
verify the existence of emotion influence in the image networks, and then
propose a probabilistic factor graph based emotion influence model to answer
the questions of "who influences whom". Employing a real network from Flickr as
experimental data, we study the effectiveness of factors in the proposed model
with in-depth data analysis. Our experiments also show that our model, by
incorporating the emotion influence, can significantly improve the accuracy
(+5%) for predicting emotions from images. Finally, a case study is used as the
anecdotal evidence to further demonstrate the effectiveness of the proposed
model
Truss Decomposition in Massive Networks
The k-truss is a type of cohesive subgraphs proposed recently for the study
of networks. While the problem of computing most cohesive subgraphs is NP-hard,
there exists a polynomial time algorithm for computing k-truss. Compared with
k-core which is also efficient to compute, k-truss represents the "core" of a
k-core that keeps the key information of, while filtering out less important
information from, the k-core. However, existing algorithms for computing
k-truss are inefficient for handling today's massive networks. We first improve
the existing in-memory algorithm for computing k-truss in networks of moderate
size. Then, we propose two I/O-efficient algorithms to handle massive networks
that cannot fit in main memory. Our experiments on real datasets verify the
efficiency of our algorithms and the value of k-truss.Comment: VLDB201
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