6,048 research outputs found
Minimizing Polarization in Noisy Leader-Follower Opinion Dynamics
The operation of creating edges has been widely applied to optimize relevant
quantities of opinion dynamics. In this paper, we consider a problem of
polarization optimization for the leader-follower opinion dynamics in a noisy
social network with nodes and edges, where a group of nodes are
leaders, and the remaining nodes are followers. We adopt the popular
leader-follower DeGroot model, where the opinion of every leader is identical
and remains unchanged, while the opinion of every follower is subject to white
noise. The polarization is defined as the steady-state variance of the
deviation of each node's opinion from leaders' opinion, which equals one half
of the effective resistance between the node group and all
other nodes. Concretely, we propose and study the problem of minimizing
by adding new edges with each incident to a node in . We
show that the objective function is monotone and supermodular. We then propose
a simple greedy algorithm with an approximation factor that
approximately solves the problem in time. To speed up the
computation, we also provide a fast algorithm to compute
(1-1/e-\eps)-approximate effective resistance , the running
time of which is \Otil (mk\eps^{-2}) for any \eps>0, where the \Otil
(\cdot) notation suppresses the factors. Extensive
experiment results show that our second algorithm is both effective and
efficient.Comment: This paper has been accepted in CIKM'23 conferenc
Updating and downdating techniques for optimizing network communicability
The total communicability of a network (or graph) is defined as the sum of
the entries in the exponential of the adjacency matrix of the network, possibly
normalized by the number of nodes. This quantity offers a good measure of how
easily information spreads across the network, and can be useful in the design
of networks having certain desirable properties. The total communicability can
be computed quickly even for large networks using techniques based on the
Lanczos algorithm.
In this work we introduce some heuristics that can be used to add, delete, or
rewire a limited number of edges in a given sparse network so that the modified
network has a large total communicability. To this end, we introduce new edge
centrality measures which can be used to guide in the selection of edges to be
added or removed.
Moreover, we show experimentally that the total communicability provides an
effective and easily computable measure of how "well-connected" a sparse
network is.Comment: 20 pages, 9 pages Supplementary Materia
RETAIL DATA ANALYTICS USING GRAPH DATABASE
Big data is an area focused on storing, processing and visualizing huge amount of data. Today data is growing faster than ever before. We need to find the right tools and applications and build an environment that can help us to obtain valuable insights from the data. Retail is one of the domains that collects huge amount of transaction data everyday. Retailers need to understand their customer’s purchasing pattern and behavior in order to take better business decisions.
Market basket analysis is a field in data mining, that is focused on discovering patterns in retail’s transaction data. Our goal is to find tools and applications that can be used by retailers to quickly understand their data and take better business decisions. Due to the amount and complexity of data, it is not possible to do such activities manually. We witness that trends change very quickly and retailers want to be quick in adapting the change and taking actions. This needs automation of processes and using algorithms that are efficient and fast. In our work, we mine transaction data by modeling the data as graphs. We use clustering algorithms to discover communities (clusters) in the data and then use the clusters for building a recommendation system that can recommend products to customers based on their buying behavior
Transforming Graph Representations for Statistical Relational Learning
Relational data representations have become an increasingly important topic
due to the recent proliferation of network datasets (e.g., social, biological,
information networks) and a corresponding increase in the application of
statistical relational learning (SRL) algorithms to these domains. In this
article, we examine a range of representation issues for graph-based relational
data. Since the choice of relational data representation for the nodes, links,
and features can dramatically affect the capabilities of SRL algorithms, we
survey approaches and opportunities for relational representation
transformation designed to improve the performance of these algorithms. This
leads us to introduce an intuitive taxonomy for data representation
transformations in relational domains that incorporates link transformation and
node transformation as symmetric representation tasks. In particular, the
transformation tasks for both nodes and links include (i) predicting their
existence, (ii) predicting their label or type, (iii) estimating their weight
or importance, and (iv) systematically constructing their relevant features. We
motivate our taxonomy through detailed examples and use it to survey and
compare competing approaches for each of these tasks. We also discuss general
conditions for transforming links, nodes, and features. Finally, we highlight
challenges that remain to be addressed
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