3,550 research outputs found
Link prediction in Foursquare network
Foursquare is an online social network and can be represented with a
bipartite network of users and venues. A user-venue pair is connected if a user
has checked-in at that venue. In the case of Foursquare, network analysis
techniques can be used to enhance the user experience. One such technique is
link prediction, which can be used to build a personalized recommendation
system of venues. Recommendation systems in bipartite networks are very often
designed using the global ranking method and collaborative filtering. A less
known method- network based inference is also a feasible choice for link
prediction in bipartite networks and sometimes performs better than the
previous two. In this paper we test these techniques on the Foursquare network.
The best technique proves to be the network based inference. We also show that
taking into account the available metadata can be beneficial
GitHub open source project recommendation system
Hosting platforms for software projects can form collaborative social
networks and a prime example of this is GitHub which is arguably the most
popular platform of this kind. An open source project recommendation system
could be a major feature for a platform like GitHub, enabling its users to find
relevant projects in a fast and simple manner. We perform network analysis on a
constructed graph based on GitHub data and present a recommendation system that
uses link prediction
Exploiting the Structure of Bipartite Graphs for Algebraic and Spectral Graph Theory Applications
In this article, we extend several algebraic graph analysis methods to
bipartite networks. In various areas of science, engineering and commerce, many
types of information can be represented as networks, and thus the discipline of
network analysis plays an important role in these domains. A powerful and
widespread class of network analysis methods is based on algebraic graph
theory, i.e., representing graphs as square adjacency matrices. However, many
networks are of a very specific form that clashes with that representation:
They are bipartite. That is, they consist of two node types, with each edge
connecting a node of one type with a node of the other type. Examples of
bipartite networks (also called \emph{two-mode networks}) are persons and the
social groups they belong to, musical artists and the musical genres they play,
and text documents and the words they contain. In fact, any type of feature
that can be represented by a categorical variable can be interpreted as a
bipartite network. Although bipartite networks are widespread, most literature
in the area of network analysis focuses on unipartite networks, i.e., those
networks with only a single type of node. The purpose of this article is to
extend a selection of important algebraic network analysis methods to bipartite
networks, showing that many methods from algebraic graph theory can be applied
to bipartite networks with only minor modifications. We show methods for
clustering, visualization and link prediction. Additionally, we introduce new
algebraic methods for measuring the bipartivity in near-bipartite graphs.Comment: 37 pages; fixed reference
Maximum entropy approach to link prediction in bipartite networks
Within network analysis, the analytical maximum entropy framework has been
very successful for different tasks as network reconstruction and filtering. In
a recent paper, the same framework was used for link-prediction for monopartite
networks: link probabilities for all unobserved links in a graph are provided
and the most probable links are selected. Here we propose the extension of such
an approach to bipartite graphs. We test our method on two real world networks
with different topological characteristics. Our performances are compared to
state-of-the-art methods, and the results show that our entropy-based approach
has a good overall performance.Comment: 7 pages, 3 figures. This work is the output of the Complexity72h
workshop (https://complexity72h.weebly.com/), held at IMT School for Advanced
Studies in Lucca, 7-11 May 201
Whether Information Network Supplements Friendship Network
Homophily is a significant mechanism for link prediction in complex network,
of which principle describes that people with similar profiles or experiences
tend to tie with each other. In a multi-relationship network, friendship among
people has been utilized to reinforce similarity of taste for recommendation
system whose basic idea is similar to homophily, yet how the taste inversely
affects friendship prediction is little discussed. This paper contributes to
address the issue by analyzing two benchmark datasets both including user's
behavioral information of taste and friendship based on the principle of
homophily. It can be found that the creation of friendship tightly associates
with personal taste. Especially, the behavioral information of taste involving
with popular objects is much more effective to improve the performance of
friendship prediction. However, this result seems to be contradictory to the
finding in [Q.M. Zhang, et al., PLoS ONE 8(2013)e62624] that the behavior
information of taste involving with popular objects is redundant in
recommendation system. We thus discuss this inconformity to comprehensively
understand the correlation between them.Comment: 8 pages, 5 figure
On Using Network Science in Mining Developers Collaboration in Software Engineering: A Systematic Literature Review
The goal of this study is to identify, review, and analyze the published
research works that used network analysis as a tool for understanding the human
collaboration on different levels of software development. This study and its
findings are expected to be of benefit for software engineering practitioners
and researchers who are mining software repositories using tools from network
science field. We conducted a systematic literature review, in which we
analyzed a number of selected papers from different digital libraries based on
inclusion and exclusion criteria. We identified primary studies (PSs) from
4 digital libraries, then we extracted data from each PS according to a
predefined data extraction sheet. The results of our data analysis showed that
not all of the constructed networks used in the PSs were valid as the edges of
these networks did not reflect a real relationship between the entities of the
network. Additionally, the used measures in the PSs were in many cases not
suitable for the used networks. Also, the reported analysis results by the PSs
were not, in most cases, validated using any statistical model. Finally, many
of the PSs did not provide lessons or guidelines for software practitioners
that can improve the software engineering practices. Although employing network
analysis in mining developers' collaboration showed some satisfactory results
in some of the PSs, the application of network analysis needs to be conducted
more carefully. That is said, the constructed network should be representative
and meaningful, the used measure needs to be suitable for the context, and the
validation of the results should be considered. More and above, we state some
research gaps, in which network science can be applied, with some pointers to
recent advances that can be used to mine collaboration networks.Comment: International Journal of Data Mining & Knowledge Management Process
(IJDKP
N2VSCDNNR: A Local Recommender System Based on Node2vec and Rich Information Network
Recommender systems are becoming more and more important in our daily lives.
However, traditional recommendation methods are challenged by data sparsity and
efficiency, as the numbers of users, items, and interactions between the two in
many real-world applications increase fast. In this work, we propose a novel
clustering recommender system based on node2vec technology and rich information
network, namely N2VSCDNNR, to solve these challenges. In particular, we use a
bipartite network to construct the user-item network, and represent the
interactions among users (or items) by the corresponding one-mode projection
network. In order to alleviate the data sparsity problem, we enrich the network
structure according to user and item categories, and construct the one-mode
projection category network. Then, considering the data sparsity problem in the
network, we employ node2vec to capture the complex latent relationships among
users (or items) from the corresponding one-mode projection category network.
Moreover, considering the dependency on parameter settings and information loss
problem in clustering methods, we use a novel spectral clustering method, which
is based on dynamic nearest-neighbors (DNN) and a novel automatically
determining cluster number (ADCN) method that determines the cluster centers
based on the normal distribution method, to cluster the users and items
separately. After clustering, we propose the two-phase personalized
recommendation to realize the personalized recommendation of items for each
user. A series of experiments validate the outstanding performance of our
N2VSCDNNR over several advanced embedding and side information based
recommendation algorithms. Meanwhile, N2VSCDNNR seems to have lower time
complexity than the baseline methods in online recommendations, indicating its
potential to be widely applied in large-scale systems
Quantitative Function and Algorithm for Community Detection in Bipartite Networks
Community detection in complex networks is a topic of high interest in many
fields. Bipartite networks are a special type of complex networks in which
nodes are decomposed into two disjoint sets, and only nodes between the two
sets can be connected. Bipartite networks represent diverse interaction
patterns in many real-world systems, such as predator-prey networks,
plant-pollinator networks, and drug-target networks. While community detection
in unipartite networks has been extensively studied in the past decade,
identification of modules or communities in bipartite networks is still in its
early stage. Several quantitative functions proposed for evaluating the quality
of bipartite network divisions are based on null models and have distinct
resolution limits. In this paper, we propose a new quantitative function for
community detection in bipartite networks, and demonstrate that this
quantitative function is superior to the widely used Barber's bipartite
modularity and other functions. Based on the new quantitative function, the
bipartite network community detection problem is formulated into an integer
programming model. Bipartite networks can be partitioned into reasonable
overlapping communities by maximizing the quantitative function. We further
develop a heuristic and adapted label propagation algorithm (BiLPA) to optimize
the quantitative function in large-scale bipartite networks. BiLPA does not
require any prior knowledge about the number of communities in the networks. We
apply BiLPA to both artificial networks and real-world networks and demonstrate
that this method can successfully identify the community structures of
bipartite networks.Comment: 18 pages, 5 figure
Bayesian one-mode projection for dynamic bipartite graphs
We propose a Bayesian methodology for one-mode projecting a bipartite network
that is being observed across a series of discrete time steps. The resulting
one mode network captures the uncertainty over the presence/absence of each
link and provides a probability distribution over its possible weight values.
Additionally, the incorporation of prior knowledge over previous states makes
the resulting network less sensitive to noise and missing observations that
usually take place during the data collection process. The methodology consists
of computationally inexpensive update rules and is scalable to large problems,
via an appropriate distributed implementation.Comment: 11 pages, 5 figure
FOBE and HOBE: First- and High-Order Bipartite Embeddings
Typical graph embeddings may not capture type-specific bipartite graph
features that arise in such areas as recommender systems, data visualization,
and drug discovery. Machine learning methods utilized in these applications
would be better served with specialized embedding techniques. We propose two
embeddings for bipartite graphs that decompose edges into sets of indirect
relationships between node neighborhoods. When sampling higher-order
relationships, we reinforce similarities through algebraic distance on graphs.
We also introduce ensemble embeddings to combine both into a "best of both
worlds" embedding. The proposed methods are evaluated on link prediction and
recommendation tasks and compared with other state-of-the-art embeddings. While
being all highly beneficial in applications, we demonstrate that none of the
considered embeddings is clearly superior (in contrast to what is claimed in
many papers), and discuss the trade offs present among them. Reproducibility:
Our code, data sets, and results are all publicly available online at:
http://sybrandt.com/2020/fobe_hobe
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