123,111 research outputs found
DPPIN: A Biological Dataset of Dynamic Protein-Protein Interaction Networks
Nowadays, many network representation learning algorithms and downstream
network mining tasks have already paid attention to dynamic networks or
temporal networks, which are more suitable for real-world complex scenarios by
modeling evolving patterns and temporal dependencies between node interactions.
Moreover, representing and mining temporal networks have a wide range of
applications, such as fraud detection, social network analysis, and drug
discovery. To contribute to the network representation learning and network
mining research community, in this paper, we generate a new biological dataset
of dynamic protein-protein interaction networks (i.e., DPPIN), which consists
of twelve dynamic protein-level interaction networks of yeast cells at
different scales. We first introduce the generation process of DPPIN. To
demonstrate the value of our published dataset DPPIN, we then list the
potential applications that would be benefited. Furthermore, we design dynamic
local clustering, dynamic spectral clustering, dynamic subgraph matching,
dynamic node classification, and dynamic graph classification experiments,
where DPPIN indicates future research opportunities for some tasks by
presenting challenges on state-of-the-art baseline algorithms. Finally, we
identify future directions for improving this dataset utility and welcome
inputs from the community. All resources of this work are deployed and publicly
available at https://github.com/DongqiFu/DPPIN
Linking Through Time: Memory-Enhanced Community Discovery in Temporal Networks
Temporal Networks, and more specifically, Markovian Temporal Networks,
present a unique challenge regarding the community discovery task. The inherent
dynamism of these systems requires an intricate understanding of memory effects
and structural heterogeneity, which are often key drivers of network evolution.
In this study, we address these aspects by introducing an innovative approach
to community detection, centered around a novel modularity function. We focus
on demonstrating the improvements our new approach brings to a fundamental
aspect of community detection: the detectability threshold problem. We show
that by associating memory directly with nodes' memberships and considering it
in the expression of the modularity, the detectability threshold can be lowered
with respect to cases where memory is not considered, thereby enhancing the
quality of the communities discovered. To validate our approach, we carry out
extensive numerical simulations, assessing the effectiveness of our method in a
controlled setting. Furthermore, we apply our method to real-world data to
underscore its practicality and robustness. This application not only
demonstrates the method's effectiveness but also reveals its capacity to
indirectly tackle additional challenges, such as determining the optimal time
window for aggregating data in dynamic graphs. This illustrates the method's
versatility in addressing complex aspects of temporal network analysis
Quality of Information in Mobile Crowdsensing: Survey and Research Challenges
Smartphones have become the most pervasive devices in people's lives, and are
clearly transforming the way we live and perceive technology. Today's
smartphones benefit from almost ubiquitous Internet connectivity and come
equipped with a plethora of inexpensive yet powerful embedded sensors, such as
accelerometer, gyroscope, microphone, and camera. This unique combination has
enabled revolutionary applications based on the mobile crowdsensing paradigm,
such as real-time road traffic monitoring, air and noise pollution, crime
control, and wildlife monitoring, just to name a few. Differently from prior
sensing paradigms, humans are now the primary actors of the sensing process,
since they become fundamental in retrieving reliable and up-to-date information
about the event being monitored. As humans may behave unreliably or
maliciously, assessing and guaranteeing Quality of Information (QoI) becomes
more important than ever. In this paper, we provide a new framework for
defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the
current state-of-the-art on the topic. We also outline novel research
challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN
From Sensor to Observation Web with Environmental Enablers in the Future Internet
This paper outlines the grand challenges in global sustainability research and the objectives of the FP7 Future Internet PPP program within the Digital Agenda for Europe. Large user communities are generating significant amounts of valuable environmental observations at local and regional scales using the devices and services of the Future Internet. These communitiesâ environmental observations represent a wealth of information which is currently hardly used or used only in isolation and therefore in need of integration with other information sources. Indeed, this very integration will lead to a paradigm shift from a mere Sensor Web to an Observation Web with semantically enriched content emanating from sensors, environmental simulations and citizens. The paper also describes the research challenges to realize the Observation Web and the associated environmental enablers for the Future Internet. Such an environmental enabler could for instance be an electronic sensing device, a web-service application, or even a social networking group affording or facilitating the capability of the Future Internet applications to consume, produce, and use environmental observations in cross-domain applications. The term ?envirofied? Future Internet is coined to describe this overall target that forms a cornerstone of work in the Environmental Usage Area within the Future Internet PPP program. Relevant trends described in the paper are the usage of ubiquitous sensors (anywhere), the provision and generation of information by citizens, and the convergence of real and virtual realities to convey understanding of environmental observations. The paper addresses the technical challenges in the Environmental Usage Area and the need for designing multi-style service oriented architecture. Key topics are the mapping of requirements to capabilities, providing scalability and robustness with implementing context aware information retrieval. Another essential research topic is handling data fusion and model based computation, and the related propagation of information uncertainty. Approaches to security, standardization and harmonization, all essential for sustainable solutions, are summarized from the perspective of the Environmental Usage Area. The paper concludes with an overview of emerging, high impact applications in the environmental areas concerning land ecosystems (biodiversity), air quality (atmospheric conditions) and water ecosystems (marine asset management)
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