Link prediction based on network embedding and similarity transferring methods

2020 ◽  
Vol 34 (16) ◽  
pp. 2050169
Author(s):  
Wei Yu ◽  
Xiaoyu Liu ◽  
Bo Ouyang
Keyword(s):  
Real World ◽  
Link Prediction ◽  
Free Parameter ◽  
Network Science ◽  
Ad Hoc ◽  
Prediction Algorithm ◽  
Network Embedding ◽  
Science Community ◽  
The Cost ◽  
Accuracy Of Prediction

In network science, link prediction is a technique used to predict missing or future relationships based on currently observed connections. Much attention from the network science community is paid to this direction recently. However, most present approaches predict links based on ad hoc similarity definitions. To address this issue, we propose a link prediction algorithm named Transferring Similarity Based on Adjacency Embedding (TSBAE). TSBAE is based on network embedding, where the potential information of the structure is preserved in the embedded vector space, and the similarity is inherently captured by the distance of these vectors. Furthermore, to accommodate the fact that the similarity should be transferable, indirect similarity between nodes is incorporated to improve the accuracy of prediction. The experimental results on 10 real-world networks show that TSBAE outperforms the baseline algorithms in the task of link prediction, with the cost of tuning a free parameter in the prediction.

scholarly journals Higher-order temporal network effects through triplet evolution

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Qing Yao ◽  
Bingsheng Chen ◽  
Tim S. Evans ◽  
Kim Christensen
Keyword(s):  
Real World ◽  
Link Prediction ◽  
Higher Order ◽  
Prediction Algorithm ◽  
Interaction Patterns ◽  
Temporal Networks ◽  
World Systems ◽  
Order Interaction ◽  
Space And Time ◽  
Pairwise Interactions

AbstractWe study the evolution of networks through ‘triplets’—three-node graphlets. We develop a method to compute a transition matrix to describe the evolution of triplets in temporal networks. To identify the importance of higher-order interactions in the evolution of networks, we compare both artificial and real-world data to a model based on pairwise interactions only. The significant differences between the computed matrix and the calculated matrix from the fitted parameters demonstrate that non-pairwise interactions exist for various real-world systems in space and time, such as our data sets. Furthermore, this also reveals that different patterns of higher-order interaction are involved in different real-world situations. To test our approach, we then use these transition matrices as the basis of a link prediction algorithm. We investigate our algorithm’s performance on four temporal networks, comparing our approach against ten other link prediction methods. Our results show that higher-order interactions in both space and time play a crucial role in the evolution of networks as we find our method, along with two other methods based on non-local interactions, give the best overall performance. The results also confirm the concept that the higher-order interaction patterns, i.e., triplet dynamics, can help us understand and predict the evolution of different real-world systems.

scholarly journals ALPINE: Active Link Prediction Using Network Embedding

2021 ◽  
Vol 11 (11) ◽  
pp. 5043
Author(s):  
Xi Chen ◽  
Bo Kang ◽  
Jefrey Lijffijt ◽  
Tijl De Bie
Keyword(s):  
Active Learning ◽  
Protein Interactions ◽  
Link Prediction ◽  
Prediction Accuracy ◽  
Real Data ◽  
Network Embedding ◽  
Protein Protein Interactions ◽  
Additional Information ◽  
The Cost ◽  
Active Link

Many real-world problems can be formalized as predicting links in a partially observed network. Examples include Facebook friendship suggestions, the prediction of protein–protein interactions, and the identification of hidden relationships in a crime network. Several link prediction algorithms, notably those recently introduced using network embedding, are capable of doing this by just relying on the observed part of the network. Often, whether two nodes are linked can be queried, albeit at a substantial cost (e.g., by questionnaires, wet lab experiments, or undercover work). Such additional information can improve the link prediction accuracy, but owing to the cost, the queries must be made with due consideration. Thus, we argue that an active learning approach is of great potential interest and developed ALPINE (Active Link Prediction usIng Network Embedding), a framework that identifies the most useful link status by estimating the improvement in link prediction accuracy to be gained by querying it. We proposed several query strategies for use in combination with ALPINE, inspired by the optimal experimental design and active learning literature. Experimental results on real data not only showed that ALPINE was scalable and boosted link prediction accuracy with far fewer queries, but also shed light on the relative merits of the strategies, providing actionable guidance for practitioners.

scholarly journals Prediction Algorithm for Mobile Ad Hoc Network Connection Breaks

2020 ◽  
Vol 12 (6) ◽  
pp. 49-63
Author(s):  
Yasir Mohammed ◽  
Maha Abdelhaq ◽  
Raed Alsaqour
Keyword(s):  
Ad Hoc Network ◽  
Link Prediction ◽  
Network Performance ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Mobile Node ◽  
Prediction Algorithm ◽  
Delivery Ratio ◽  
Network Simulator ◽  
Mobile Ad Hoc

A Mobile Ad-Hoc Network (MANET) is a decentralized network of mobile node that are connected to an arbitrary topology via wireless connections. The breakdown of the connecting links between adjacent nodes will probably lead to the loss of the transferred data packets. In this research, we proposed an algorithm for link prediction (LP) to enhance the link break provision of the ad hoc on-demand remote protocol (AODV). The proposed algorithm is called the AODV Link Break Prediction (AODVLBP). The AODVLBP prevents link breaks by the use of a predictive measure of the changing signal. The AODVLBP was evaluated using the network simulator version 2.35 (NS2) and compared with the AODV Link prediction (AODVLP) and the AODV routing protocols. The simulation results reveal the effectiveness of AODVLBP in improving network performance in terms of average end-to-end delay, packet delivery ratio, packet overhead ratio, and packet drop-neighbour break.

scholarly journals Transaction Prediction in Blockchain: A Negative Link Prediction Algorithm Based on the Sentiment Analysis and Balance Theory

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Ling Yuan ◽  
JiaLi Bin ◽  
YinZhen Wei ◽  
Zhihua Hu ◽  
Ping Sun
Keyword(s):  
Sentiment Analysis ◽  
Link Prediction ◽  
User Interaction ◽  
Interaction Matrix ◽  
Prediction Algorithm ◽  
Balance Theory ◽  
User Interactions ◽  
Negative Links ◽  
Accuracy Of Prediction ◽  
Negative Link

User relationship prediction in the transaction of Blockchain is to predict whether a transaction will occur between two users in the future, which can be abstracted into the link prediction problem. The link prediction can be categorized into the positive one and the negative one. However, the existing negative link prediction algorithms mainly consider the number of negative user interactions and lack the full use of emotion characteristics in user interactions. To solve this problem, this paper proposes a negative link prediction algorithm based on the sentiment analysis and balance theory. Firstly, the user interaction matrix is constructed based on calculating the intensity of emotion polarity for social network texts, and a reliability weight matrix (noted as RW-matrix) is constructed based on the user interaction matrix to measure the reliability of negative links. Secondly, with the RW-matrix, a negative link prediction algorithm is proposed based on the structural balance theory by constructing negative link sample sets and extracting sample features. To evaluate the performance of the negative link prediction algorithm proposed, the variable management method is used to analyze the influence of negative sample control error and other parameters on the accuracy of it. Compared with the existing prediction benchmark algorithms, the experimental results demonstrate that the proposed negative link prediction algorithm can improve the accuracy of prediction significantly and deliver good performances.

scholarly journals Learning Signed Network Embedding via Graph Attention

2020 ◽  
Vol 34 (04) ◽  
pp. 4772-4779 ◽  
Author(s):  
Yu Li ◽  
Yuan Tian ◽  
Jiawei Zhang ◽  
Yi Chang
Keyword(s):  
Network Analysis ◽  
Real World ◽  
Link Prediction ◽  
Critical Role ◽  
Network Embedding ◽  
Convolutional Networks ◽  
Importance Coefficient ◽  
Signed Network ◽  
Low Dimensional ◽  
Negative Links

Learning the low-dimensional representations of graphs (i.e., network embedding) plays a critical role in network analysis and facilitates many downstream tasks. Recently graph convolutional networks (GCNs) have revolutionized the field of network embedding, and led to state-of-the-art performance in network analysis tasks such as link prediction and node classification. Nevertheless, most of the existing GCN-based network embedding methods are proposed for unsigned networks. However, in the real world, some of the networks are signed, where the links are annotated with different polarities, e.g., positive vs. negative. Since negative links may have different properties from the positive ones and can also significantly affect the quality of network embedding. Thus in this paper, we propose a novel network embedding framework SNEA to learn Signed Network Embedding via graph Attention. In particular, we propose a masked self-attentional layer, which leverages self-attention mechanism to estimate the importance coefficient for pair of nodes connected by different type of links during the embedding aggregation process. Then SNEA utilizes the masked self-attentional layers to aggregate more important information from neighboring nodes to generate the node embeddings based on balance theory. Experimental results demonstrate the effectiveness of the proposed framework through signed link prediction task on several real-world signed network datasets.

A study of cane supply benefits associated with ad hoc mechanical harvesting to mitigate cutter absenteeism

2013 ◽  
pp. 532-538 ◽  
Author(s):  
Muhammad Kadwa ◽  
Carel N Bezuidenhout
Keyword(s):  
Supply Chain ◽  
South African ◽  
Ad Hoc ◽  
Sugar Mill ◽  
The South ◽  
Optimum Performance ◽  
Kwazulu Natal ◽  
Cane Quality ◽  
The Cost

The Eston Sugar Mill is the newest in the South African KwaZulu-Natal sugar belt. Like most other mills, it can be argued that there are inefficiencies in the supply chain due to systematic issues, which reduce optimum performance. It was alleged that mill processes are slowed, or stopped, on Sundays, Mondays, as well as some Tuesdays and Wednesdays, due to pay-weekends, because of the associated cutter absenteeism. This increases the length of the milling season (LOMS), increases milling costs and reduces the average cane quality for the season. Data on cane deliveries to the Eston Mill, over a period of five seasons, were analysed to study the magnitude of the problem. It was statistically verified that cane shortages occur immediately after payweekends and it was conservatively estimated that cutter absenteeism occurs between 25–29 days per season, which increases the LOMS by six to ten days. The associated cost of this problem equated to an average of US$159,500 (approximately EUR120,000) per milling season. In this paper, an alternative harvesting system scenario is suggested, assuming that mechanical harvesters be used after a pay-weekend, to mitigate the impacts of cutter shortages. However, the solution is calculated to be risky. When the cost of new equipment was considered, only two of the five seasons were able to justify the associated costs.

W-MMP2Vec: Topic-driven network embedding model for link prediction in content-based heterogeneous information network

2021 ◽  
Vol 25 (3) ◽  
pp. 711-738
Author(s):  
Phu Pham ◽  
Phuc Do
Keyword(s):  
Link Prediction ◽  
Representation Learning ◽  
Information Network ◽  
Network Embedding ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Learning Framework ◽  
Novel Approach ◽  
Proposed Model ◽  
Meta Path

Link prediction on heterogeneous information network (HIN) is considered as a challenge problem due to the complexity and diversity in types of nodes and links. Currently, there are remained challenges of meta-path-based link prediction in HIN. Previous works of link prediction in HIN via network embedding approach are mainly focused on exploiting features of node rather than existing relations in forms of meta-paths between nodes. In fact, predicting the existence of new links between non-linked nodes is absolutely inconvincible. Moreover, recent HIN-based embedding models also lack of thorough evaluations on the topic similarity between text-based nodes along given meta-paths. To tackle these challenges, in this paper, we proposed a novel approach of topic-driven multiple meta-path-based HIN representation learning framework, namely W-MMP2Vec. Our model leverages the quality of node representations by combining multiple meta-paths as well as calculating the topic similarity weight for each meta-path during the processes of network embedding learning in content-based HINs. To validate our approach, we apply W-TMP2Vec model in solving several link prediction tasks in both content-based and non-content-based HINs (DBLP, IMDB and BlogCatalog). The experimental outputs demonstrate the effectiveness of proposed model which outperforms recent state-of-the-art HIN representation learning models.

The Cost-Effectiveness of Pre-school Peanut Oral Immunotherapy in the Real World Setting

2021 ◽  
Author(s):  
Marcus Shaker ◽  
Edmond S. Chan ◽  
Jennifer LP. Protudjer ◽  
Lianne Soller ◽  
Elissa M. Abrams ◽  
...  
Keyword(s):  
Cost Effectiveness ◽  
Real World ◽  
Oral Immunotherapy ◽  
The Real ◽  
Real World Setting ◽  
The Cost

PP469 From Theory To Practice: Which Value Framework Is Applied For Onco-Hematology Therapies In Italy? A 5-year Retrospective Analysis

2020 ◽  
Vol 36 (S1) ◽  
pp. 37-37
Author(s):  
Americo Cicchetti ◽  
Rossella Di Bidino ◽  
Entela Xoxi ◽  
Irene Luccarini ◽  
Alessia Brigido
Keyword(s):  
Real World ◽  
Ad Hoc ◽  
Grey Literature ◽  
National Level ◽  
Pharmaceutical Products ◽  
European Medicines Agency ◽  
The Real ◽  
R Process ◽  
The One ◽  
The Impact

IntroductionDifferent value frameworks (VFs) have been proposed in order to translate available evidence on risk-benefit profiles of new treatments into Pricing & Reimbursement (P&R) decisions. However limited evidence is available on the impact of their implementation. It's relevant to distinguish among VFs proposed by scientific societies and providers, which usually are applicable to all treatments, and VFs elaborated by regulatory agencies and health technology assessment (HTA), which focused on specific therapeutic areas. Such heterogeneity in VFs has significant implications in terms of value dimension considered and criteria adopted to define or support a price decision.MethodsA literature research was conducted to identify already proposed or adopted VF for onco-hematology treatments. Both scientific and grey literature were investigated. Then, an ad hoc data collection was conducted for multiple myeloma; breast, prostate and urothelial cancer; and Non Small Cell Lung Cancer (NSCLC) therapies. Pharmaceutical products authorized by European Medicines Agency from January 2014 till December 2019 were identified. Primary sources of data were European Public Assessment Reports and P&R decision taken by the Italian Medicines Agency (AIFA) till September 2019.ResultsThe analysis allowed to define a taxonomy to distinguish categories of VF relevant to onco-hematological treatments. We identified the “real-world” VF that emerged given past P&R decisions taken at the Italian level. Data was collected both for clinical and economical outcomes/indicators, as well as decisions taken on innovativeness of therapies. Relevant differences emerge between the real world value framework and the one that should be applied given the normative framework of the Italian Health System.ConclusionsThe value framework that emerged from the analysis addressed issues of specific aspects of onco-hematological treatments which emerged during an ad hoc analysis conducted on treatment authorized in the last 5 years. The perspective adopted to elaborate the VF was the one of an HTA agency responsible for P&R decisions at a national level. Furthermore, comparing a real-world value framework with the one based on the general criteria defined by the national legislation, our analysis allowed identification of the most critical point of the current national P&R process in terms ofsustainability of current and future therapies as advance therapies and agnostic-tumor therapies.

Sign in / Sign up

Export Citation Format

Share Document