4,148 research outputs found
Artificial Intelligence for Complex Network: Potential, Methodology and Application
Complex networks pervade various real-world systems, from the natural
environment to human societies. The essence of these networks is in their
ability to transition and evolve from microscopic disorder-where network
topology and node dynamics intertwine-to a macroscopic order characterized by
certain collective behaviors. Over the past two decades, complex network
science has significantly enhanced our understanding of the statistical
mechanics, structures, and dynamics underlying real-world networks. Despite
these advancements, there remain considerable challenges in exploring more
realistic systems and enhancing practical applications. The emergence of
artificial intelligence (AI) technologies, coupled with the abundance of
diverse real-world network data, has heralded a new era in complex network
science research. This survey aims to systematically address the potential
advantages of AI in overcoming the lingering challenges of complex network
research. It endeavors to summarize the pivotal research problems and provide
an exhaustive review of the corresponding methodologies and applications.
Through this comprehensive survey-the first of its kind on AI for complex
networks-we expect to provide valuable insights that will drive further
research and advancement in this interdisciplinary field.Comment: 51 pages, 4 figures, 10 table
Target-Tailored Source-Transformation for Scene Graph Generation
Scene graph generation aims to provide a semantic and structural description
of an image, denoting the objects (with nodes) and their relationships (with
edges). The best performing works to date are based on exploiting the context
surrounding objects or relations,e.g., by passing information among objects. In
these approaches, to transform the representation of source objects is a
critical process for extracting information for the use by target objects. In
this work, we argue that a source object should give what tar-get object needs
and give different objects different information rather than contributing
common information to all targets. To achieve this goal, we propose a
Target-TailoredSource-Transformation (TTST) method to efficiently propagate
information among object proposals and relations. Particularly, for a source
object proposal which will contribute information to other target objects, we
transform the source object feature to the target object feature domain by
simultaneously taking both the source and target into account. We further
explore more powerful representations by integrating language prior with the
visual context in the transformation for the scene graph generation. By doing
so the target object is able to extract target-specific information from the
source object and source relation accordingly to refine its representation. Our
framework is validated on the Visual Genome bench-mark and demonstrated its
state-of-the-art performance for the scene graph generation. The experimental
results show that the performance of object detection and visual relation-ship
detection are promoted mutually by our method
Reflective visualization and verbalization of unconscious preference
A new method is presented, that can help a person become aware of his or her
unconscious preferences, and convey them to others in the form of verbal
explanation. The method combines the concepts of reflection, visualization, and
verbalization. The method was tested in an experiment where the unconscious
preferences of the subjects for various artworks were investigated. In the
experiment, two lessons were learned. The first is that it helps the subjects
become aware of their unconscious preferences to verbalize weak preferences as
compared with strong preferences through discussion over preference diagrams.
The second is that it is effective to introduce an adjustable factor into
visualization to adapt to the differences in the subjects and to foster their
mutual understanding.Comment: This will be submitted to KES Journa
Exploring Causal Learning through Graph Neural Networks: An In-depth Review
In machine learning, exploring data correlations to predict outcomes is a
fundamental task. Recognizing causal relationships embedded within data is
pivotal for a comprehensive understanding of system dynamics, the significance
of which is paramount in data-driven decision-making processes. Beyond
traditional methods, there has been a surge in the use of graph neural networks
(GNNs) for causal learning, given their capabilities as universal data
approximators. Thus, a thorough review of the advancements in causal learning
using GNNs is both relevant and timely. To structure this review, we introduce
a novel taxonomy that encompasses various state-of-the-art GNN methods employed
in studying causality. GNNs are further categorized based on their applications
in the causality domain. We further provide an exhaustive compilation of
datasets integral to causal learning with GNNs to serve as a resource for
practical study. This review also touches upon the application of causal
learning across diverse sectors. We conclude the review with insights into
potential challenges and promising avenues for future exploration in this
rapidly evolving field of machine learning
Temporal Networks
A great variety of systems in nature, society and technology -- from the web
of sexual contacts to the Internet, from the nervous system to power grids --
can be modeled as graphs of vertices coupled by edges. The network structure,
describing how the graph is wired, helps us understand, predict and optimize
the behavior of dynamical systems. In many cases, however, the edges are not
continuously active. As an example, in networks of communication via email,
text messages, or phone calls, edges represent sequences of instantaneous or
practically instantaneous contacts. In some cases, edges are active for
non-negligible periods of time: e.g., the proximity patterns of inpatients at
hospitals can be represented by a graph where an edge between two individuals
is on throughout the time they are at the same ward. Like network topology, the
temporal structure of edge activations can affect dynamics of systems
interacting through the network, from disease contagion on the network of
patients to information diffusion over an e-mail network. In this review, we
present the emergent field of temporal networks, and discuss methods for
analyzing topological and temporal structure and models for elucidating their
relation to the behavior of dynamical systems. In the light of traditional
network theory, one can see this framework as moving the information of when
things happen from the dynamical system on the network, to the network itself.
Since fundamental properties, such as the transitivity of edges, do not
necessarily hold in temporal networks, many of these methods need to be quite
different from those for static networks
Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases
Mapping perturbed molecular circuits that underlie complex diseases remains a great challenge. We developed a comprehensive resource of 394 cell type– and tissue-specific gene regulatory networks for human, each specifying the genome-wide connectivity among transcription factors, enhancers, promoters and genes. Integration with 37 genome-wide association studies (GWASs) showed that disease-associated genetic variants—including variants that do not reach genome-wide significance—often perturb regulatory modules that are highly specific to disease-relevant cell types or tissues. Our resource opens the door to systematic analysis of regulatory programs across hundreds of human cell types and tissue
MonoDiffusion: Self-Supervised Monocular Depth Estimation Using Diffusion Model
Over the past few years, self-supervised monocular depth estimation that does
not depend on ground-truth during the training phase has received widespread
attention. Most efforts focus on designing different types of network
architectures and loss functions or handling edge cases, e.g., occlusion and
dynamic objects. In this work, we introduce a novel self-supervised depth
estimation framework, dubbed MonoDiffusion, by formulating it as an iterative
denoising process. Because the depth ground-truth is unavailable in the
training phase, we develop a pseudo ground-truth diffusion process to assist
the diffusion in MonoDiffusion. The pseudo ground-truth diffusion gradually
adds noise to the depth map generated by a pre-trained teacher model.
Moreover,the teacher model allows applying a distillation loss to guide the
denoised depth. Further, we develop a masked visual condition mechanism to
enhance the denoising ability of model. Extensive experiments are conducted on
the KITTI and Make3D datasets and the proposed MonoDiffusion outperforms prior
state-of-the-art competitors. The source code will be available at
https://github.com/ShuweiShao/MonoDiffusion.Comment: 10 pages, 8 figure
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