35,863 research outputs found
On Counting Triangles through Edge Sampling in Large Dynamic Graphs
Traditional frameworks for dynamic graphs have relied on processing only the
stream of edges added into or deleted from an evolving graph, but not any
additional related information such as the degrees or neighbor lists of nodes
incident to the edges. In this paper, we propose a new edge sampling framework
for big-graph analytics in dynamic graphs which enhances the traditional model
by enabling the use of additional related information. To demonstrate the
advantages of this framework, we present a new sampling algorithm, called Edge
Sample and Discard (ESD). It generates an unbiased estimate of the total number
of triangles, which can be continuously updated in response to both edge
additions and deletions. We provide a comparative analysis of the performance
of ESD against two current state-of-the-art algorithms in terms of accuracy and
complexity. The results of the experiments performed on real graphs show that,
with the help of the neighborhood information of the sampled edges, the
accuracy achieved by our algorithm is substantially better. We also
characterize the impact of properties of the graph on the performance of our
algorithm by testing on several Barabasi-Albert graphs.Comment: A short version of this article appeared in Proceedings of the 2017
IEEE/ACM International Conference on Advances in Social Networks Analysis and
Mining (ASONAM 2017
CryptGraph: Privacy Preserving Graph Analytics on Encrypted Graph
Many graph mining and analysis services have been deployed on the cloud,
which can alleviate users from the burden of implementing and maintaining graph
algorithms. However, putting graph analytics on the cloud can invade users'
privacy. To solve this problem, we propose CryptGraph, which runs graph
analytics on encrypted graph to preserve the privacy of both users' graph data
and the analytic results. In CryptGraph, users encrypt their graphs before
uploading them to the cloud. The cloud runs graph analysis on the encrypted
graphs and obtains results which are also in encrypted form that the cloud
cannot decipher. During the process of computing, the encrypted graphs are
never decrypted on the cloud side. The encrypted results are sent back to users
and users perform the decryption to obtain the plaintext results. In this
process, users' graphs and the analytics results are both encrypted and the
cloud knows neither of them. Thereby, users' privacy can be strongly protected.
Meanwhile, with the help of homomorphic encryption, the results analyzed from
the encrypted graphs are guaranteed to be correct. In this paper, we present
how to encrypt a graph using homomorphic encryption and how to query the
structure of an encrypted graph by computing polynomials. To solve the problem
that certain operations are not executable on encrypted graphs, we propose hard
computation outsourcing to seek help from users. Using two graph algorithms as
examples, we show how to apply our methods to perform analytics on encrypted
graphs. Experiments on two datasets demonstrate the correctness and feasibility
of our methods
Dynamic Influence Networks for Rule-based Models
We introduce the Dynamic Influence Network (DIN), a novel visual analytics
technique for representing and analyzing rule-based models of protein-protein
interaction networks. Rule-based modeling has proved instrumental in developing
biological models that are concise, comprehensible, easily extensible, and that
mitigate the combinatorial complexity of multi-state and multi-component
biological molecules. Our technique visualizes the dynamics of these rules as
they evolve over time. Using the data produced by KaSim, an open source
stochastic simulator of rule-based models written in the Kappa language, DINs
provide a node-link diagram that represents the influence that each rule has on
the other rules. That is, rather than representing individual biological
components or types, we instead represent the rules about them (as nodes) and
the current influence of these rules (as links). Using our interactive DIN-Viz
software tool, researchers are able to query this dynamic network to find
meaningful patterns about biological processes, and to identify salient aspects
of complex rule-based models. To evaluate the effectiveness of our approach, we
investigate a simulation of a circadian clock model that illustrates the
oscillatory behavior of the KaiC protein phosphorylation cycle.Comment: Accepted to TVCG, in pres
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