scholarly journals Spreading predictability in complex networks

2020 ◽  
Author(s):  
Na Zhao ◽  
Jian Wang ◽  
Yong Yu ◽  
Jun-Yan Zhao ◽  
Duan-Bing Chen
Keyword(s):  
Prediction Model ◽  
Complex Networks ◽  
State Of The Art ◽  
Viral Marketing ◽  
Experimental Results ◽  
Dynamics Analysis ◽  
Interesting Topic ◽  
Spreading Dynamics

AbstractSpreading dynamics analysis is an important and interesting topic since it has many applications such as rumor or disease controlling, viral marketing and information recommending. Many state-of-the-art researches focus on predicting infection scale or threshold. Few researchers pay attention to the predicting of infection nodes from a snapshot. With developing of precision marketing, recommending and, controlling, how to predict infection nodes precisely from snapshot becomes a key issue in spreading dynamics analysis. In this paper, a probability based prediction model is presented so as to estimate the infection nodes from a snapshot of spreading. Experimental results on synthetic and real networks demonstrate that the model proposed could predict the infection nodes precisely in the sense of probability.

scholarly journals Spreading predictability in complex networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Na Zhao ◽  
Jian Wang ◽  
Yong Yu ◽  
Jun-Yan Zhao ◽  
Duan-Bing Chen
Keyword(s):  
Infectious Diseases ◽  
Prediction Model ◽  
Complex Networks ◽  
State Of The Art ◽  
Experimental Results ◽  
Vaccination Strategies ◽  
Macro Scale ◽  
Good Consistency ◽  
The Future ◽  
Rumor Control

AbstractMany state-of-the-art researches focus on predicting infection scale or threshold in infectious diseases or rumor and give the vaccination strategies correspondingly. In these works, most of them assume that the infection probability and initially infected individuals are known at the very beginning. Generally, infectious diseases or rumor has been spreading for some time when it is noticed. How to predict which individuals will be infected in the future only by knowing the current snapshot becomes a key issue in infectious diseases or rumor control. In this report, a prediction model based on snapshot is presented to predict the potentially infected individuals in the future, not just the macro scale of infection. Experimental results on synthetic and real networks demonstrate that the infected individuals predicted by the model have good consistency with the actual infected ones based on simulations.

scholarly journals Spreading Predictability in Complex Networks

2021 ◽  
Author(s):  
Na Zhao ◽  
Jian Wang ◽  
Yong Yu ◽  
Junyan Zhao ◽  
Duanbing Chen
Keyword(s):  
Infectious Diseases ◽  
Prediction Model ◽  
Complex Networks ◽  
State Of The Art ◽  
Experimental Results ◽  
Vaccination Strategies ◽  
Model Based ◽  
The Future ◽  
Rumor Control

Abstract Many state-of-the-art researches focus on predicting infection scale or threshold in infectious diseases or rumor and give the vaccination strategies correspondingly. In these works, most of them assume that the infected probability and initially infected individuals are known at the very beginning. Generally, infectious diseases or rumor has been spreading for some time when it is noticed. How to predict which individuals will be infected in the future only by knowing the current snapshot becomes a key issue in infectious diseases or rumor control. In this paper, a prediction model based on snapshot is presented to predict the potentially infected individuals in the future. Experimental results on synthetic and real networks demonstrate that the predicted infected individuals have rather consistency with the actual infected ones.

scholarly journals Improved Local Search with Momentum for Bayesian Networks Structure Learning

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 750
Author(s):  
Xiaohan Liu ◽  
Xiaoguang Gao ◽  
Zidong Wang ◽  
Xinxin Ru
Keyword(s):  
Local Search ◽  
Complex Networks ◽  
Bayesian Networks ◽  
Structure Learning ◽  
Hybrid Methods ◽  
State Of The Art ◽  
The State ◽  
Experimental Results ◽  
Local Optimum ◽  
Networks Structure

Bayesian Networks structure learning (BNSL) is a troublesome problem that aims to search for an optimal structure. An exact search tends to sacrifice a significant amount of time and memory to promote accuracy, while the local search can tackle complex networks with thousands of variables but commonly gets stuck in a local optimum. In this paper, two novel and practical operators and a derived operator are proposed to perturb structures and maintain the acyclicity. Then, we design a framework, incorporating an influential perturbation factor integrated by three proposed operators, to escape current local optimal and improve the dilemma that outcomes trap in local optimal. The experimental results illustrate that our algorithm can output competitive results compared with the state-of-the-art constraint-based method in most cases. Meanwhile, our algorithm reaches an equivalent or better solution found by the state-of-the-art exact search and hybrid methods.

scholarly journals Spreading dynamics in complex networks with applications in vehicular networks

2017 ◽  
Author(s):  
Παύλος Μπασαράς
Keyword(s):  
Ad Hoc Networks ◽  
Complex Networks ◽  
Vehicular Networks ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
State Of The Art ◽  
Power Grids ◽  
Spreading Dynamics ◽  
Hoc Networks ◽  
Self Driving Cars

Η ανάλυση σύμπλεκτων (complex) δικτύων έχει λάβει τεράστια προσοχή τα τελευταία χρόνια, εξαιτίας της ανάπτυξης των online social δικτύων, και της ύπαρξης τεραστίου όγκου δεδομένων σχετικών με τεχνολογικά (Internet, Web, power grids), βιολογικά δίκτυα (αλληλεπιδράσεων πρωτεϊνών, γονιδίων, εξάπλωση ασθενειών), κ.τ.λ. Η παρούσα διατριβή εξερεύνησε το ζήτημα της διάδοσης “πληροφορίας” (φημών, ιών,…) σε σύμπλεκτα δίκτυα, καθώς και εφαρμογές των σχετικών αποτελεσμάτων σε ad hoc δίκτυα οχημάτων. Ειδικότερα, μελετήθηκε το πρόβλημα της μεγιστοποίησης διάδοσης (influence maximization), και αναπτύχθηκαν state-of-the-art αλγορίθμοι για την εύρεση των πιο ικανών διασκορπιστών πληροφορίας (influential spreaders) σε μονοεπίπεδα και πολυεπίπεδα σύμπλεκτα δίκτυα, καθώς και προσαρμογή αυτών σε πρωτόκολλα δρομολόγησης σε ad hoc δίκτυα οχημάτων (vehicular ad hoc networks). Επιπρόσθετα, μελετήθηκε το δυϊκό πρόβλημα της ελαχιστοποίησης διάδοσης “κακοήθους” (malicious) πληροφορίας σε σύμπλεκτα δίκτυα, για πρώτη φορά σε online fashion, και αναπτύχθηκε αλγόριθμος για την επιλογή των καλύτερων παρακωλυτών διάδοσης κακοήθους πληροφορίας. Κάτω από το πρίσμα των προσφάτων εξελίξεων στα self-driving cars, το προαναφερθέν πρόβλημα μελετήθηκε και για ad hoc δίκτυα οχημάτων, και αναπτύχθηκε μια τεχνική black-listing για όσα οχήματα έχουν περάσει στον έλεγχο κάποιου “adversary”. Τέλος, μελετήθηκε και γενικεύτηκε η ισχύς του “παραδόξου της φιλίας” για μια σειρά από τοπολογικά χαρακτηριστικά των κόμβων ενός δικτύου, και αναπτύχθηκαν εφαρμογές του για την μεγιστοποίηση (ελαχιστοποίηση) της διάδοσης της πληροφορίας στα σύμπλεκτα δίκτυα.

TPmod: A Tendency-Guided Prediction Model for Temporal Knowledge Graph Completion

2021 ◽  
Vol 15 (3) ◽  
pp. 1-17
Author(s):  
Luyi Bai ◽  
Xiangnan Ma ◽  
Mingcheng Zhang ◽  
Wenting Yu
Keyword(s):  
Artificial Intelligence ◽  
Prediction Model ◽  
State Of The Art ◽  
Attention Mechanism ◽  
Experimental Results ◽  
Knowledge Graph ◽  
Modeling Process ◽  
Knowledge Graphs

Temporal knowledge graphs (TKGs) have become useful resources for numerous Artificial Intelligence applications, but they are far from completeness. Inferring missing events in temporal knowledge graphs is a fundamental and challenging task. However, most existing methods solely focus on entity features or consider the entities and relations in a disjoint manner. They do not integrate the features of entities and relations in their modeling process. In this paper, we propose TPmod, a tendency-guided prediction model, to predict the missing events for TKGs (extrapolation). Differing from existing works, we propose two definitions for TKGs: the Goodness of relations and the Closeness of entity pairs. More importantly, inspired by the attention mechanism, we propose a novel tendency strategy to guide our aggregated process. It integrates the features of entities and relations, and assigns varying weights to different past events. What is more, we select the Gate Recurrent Unit (GRU) as our sequential encoder to model the temporal dependency in TKGs. Besides, the Softmax function is employed to generate the final decreasing group of candidate entities. We evaluate our model on two TKG datasets: GDELT-5 and ICEWS-250. Experimental results show that our method has a significant and consistent improvement compared to state-of-the-art baselines.

Browser Security Attacks and Detection Techniques: A Case of Tabnabbing

2020 ◽  
Vol 8 (1) ◽  
pp. 33-41
Author(s):  
Dr. S. Sarika ◽  
Keyword(s):  
Credit Card ◽  
State Of The Art ◽  
Experimental Results ◽  
Detection Technique ◽  
Security Attacks ◽  
Agent Based ◽  
Detection Techniques ◽  
Browser Security ◽  
Cyber Threats ◽  
Multi Agent

Phishing is a malicious and deliberate act of sending counterfeit messages or mimicking a webpage. The goal is either to steal sensitive credentials like login information and credit card details or to install malware on a victim’s machine. Browser-based cyber threats have become one of the biggest concerns in networked architectures. The most prolific form of browser attack is tabnabbing which happens in inactive browser tabs. In a tabnabbing attack, a fake page disguises itself as a genuine page to steal data. This paper presents a multi agent based tabnabbing detection technique. The method detects heuristic changes in a webpage when a tabnabbing attack happens and give a warning to the user. Experimental results show that the method performs better when compared with state of the art tabnabbing detection techniques.

scholarly journals Automatic Detection of Discrimination Actions from Social Images

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 325
Author(s):  
Zhihao Wu ◽  
Baopeng Zhang ◽  
Tianchen Zhou ◽  
Yan Li ◽  
Jianping Fan
Keyword(s):  
Action Recognition ◽  
State Of The Art ◽  
Automatic Detection ◽  
Experimental Results ◽  
Practical Approach ◽  
Detection And Identification ◽  
Art Methods ◽  
Image Set ◽  
Social Images ◽  
Relationship Identification

In this paper, we developed a practical approach for automatic detection of discrimination actions from social images. Firstly, an image set is established, in which various discrimination actions and relations are manually labeled. To the best of our knowledge, this is the first work to create a dataset for discrimination action recognition and relationship identification. Secondly, a practical approach is developed to achieve automatic detection and identification of discrimination actions and relationships from social images. Thirdly, the task of relationship identification is seamlessly integrated with the task of discrimination action recognition into one single network called the Co-operative Visual Translation Embedding++ network (CVTransE++). We also compared our proposed method with numerous state-of-the-art methods, and our experimental results demonstrated that our proposed methods can significantly outperform state-of-the-art approaches.

scholarly journals Phase diagrams of interacting spreading dynamics in complex networks

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Liming Pan ◽  
Dan Yang ◽  
Wei Wang ◽  
Shimin Cai ◽  
Tao Zhou ◽  
...  
Keyword(s):  
Complex Networks ◽  
Phase Diagrams ◽  
Spreading Dynamics

scholarly journals PyConvU-Net: a lightweight and multiscale network for biomedical image segmentation

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Changyong Li ◽  
Yongxian Fan ◽  
Xiaodong Cai
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
State Of The Art ◽  
Experimental Results ◽  
Actual Situation ◽  
Controlled Experiments ◽  
Biomedical Image ◽  
Segmentation Methods ◽  
Art Performance

Abstract Background With the development of deep learning (DL), more and more methods based on deep learning are proposed and achieve state-of-the-art performance in biomedical image segmentation. However, these methods are usually complex and require the support of powerful computing resources. According to the actual situation, it is impractical that we use huge computing resources in clinical situations. Thus, it is significant to develop accurate DL based biomedical image segmentation methods which depend on resources-constraint computing. Results A lightweight and multiscale network called PyConvU-Net is proposed to potentially work with low-resources computing. Through strictly controlled experiments, PyConvU-Net predictions have a good performance on three biomedical image segmentation tasks with the fewest parameters. Conclusions Our experimental results preliminarily demonstrate the potential of proposed PyConvU-Net in biomedical image segmentation with resources-constraint computing.

scholarly journals Evaluation of recent advances in recommender systems on Arabic content

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mehdi Srifi ◽  
Ahmed Oussous ◽  
Ayoub Ait Lahcen ◽  
Salma Mouline
Keyword(s):  
Recommender Systems ◽  
High Performance ◽  
Large Scale ◽  
State Of The Art ◽  
Experimental Results ◽  
Recent Advances ◽  
Research Gap ◽  
Text Preprocessing

AbstractVarious recommender systems (RSs) have been developed over recent years, and many of them have concentrated on English content. Thus, the majority of RSs from the literature were compared on English content. However, the research investigations about RSs when using contents in other languages such as Arabic are minimal. The researchers still neglect the field of Arabic RSs. Therefore, we aim through this study to fill this research gap by leveraging the benefit of recent advances in the English RSs field. Our main goal is to investigate recent RSs in an Arabic context. For that, we firstly selected five state-of-the-art RSs devoted originally to English content, and then we empirically evaluated their performance on Arabic content. As a result of this work, we first build four publicly available large-scale Arabic datasets for recommendation purposes. Second, various text preprocessing techniques have been provided for preparing the constructed datasets. Third, our investigation derived well-argued conclusions about the usage of modern RSs in the Arabic context. The experimental results proved that these systems ensure high performance when applied to Arabic content.

Sign in / Sign up

Export Citation Format

Share Document