23 papers · ranked by Valyu relevance
Yang Yang, Ryan N. Lichtenwalter, Nitesh V. Chawla
Link prediction is a popular research area with important applications in a variety of disciplines, including biology, social science, security, and medicine. The fundamental requirement of link prediction is the accurate and effective prediction of new links in networks. While there are many different methods proposed…
Fei Tan, Yongxiang Xia, Boyao Zhu, Wang Zhan
Topological properties of networks are widely applied to study the link-prediction problem recently. Common Neighbors, for example, is a natural yet efficient framework. Many variants of Common Neighbors have been thus proposed to further boost the discriminative resolution of candidate links. In this paper, we…
Fei Tan, Yongxiang Xia, Boyao Zhu
Topological properties of networks are widely applied to study the link-prediction problem recently. Common Neighbors, for example, is a natural yet efficient framework. Many variants of Common Neighbors have been thus proposed to further boost the discriminative resolution of candidate links. In this paper, we…
Said Kerrache, José Manuel Galán
The problem of determining the likelihood of the existence of a link between two nodes in a network is called link prediction. This is made possible thanks to the existence of a topological structure in most real-life networks. In other words, the topologies of networked systems such as the World Wide Web, the…
Xu-Wen Wang, Yize Chen, Yang-Yu Liu
Inferring missing links or predicting future ones based on the currently observed network is known as link prediction, which has tremendous real-world applications in biomedicine^1–3^, e-commerce^4^, social media^5^ and criminal intelligence^6^. Numerous methods have been proposed to solve the link prediction…
Siqi Peng, Akihiro Yamamoto, Kimihito Ito, V. Vinoth Kumar
We propose a new method for bipartite link prediction using matrix factorization with negative sample selection. Bipartite link prediction is a problem that aims to predict the missing links or relations in a bipartite network. One of the most popular solutions to the problem is via matrix factorization (MF), which…
Boyao Zhu, Yongxiang Xia
Various structural features of networks have been applied to develop link prediction methods. However, because different features highlight different aspects of network structural properties, it is very difficult to benefit from all of the features that might be available. In this paper, we investigate the role of…
Mahdi Jalili, Yasin Orouskhani, Milad Asgari, Nazanin Alipourfard + 1 more
'Matjaž Perc'] Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social…
Peng Wang, Baowen Xu, Yurong Wu, Xiaoyu Zhou
In social networks, link prediction predicts missing links in current networks and new or dissolution links in future networks, is important for mining and analyzing the evolution of social networks. In the past decade, many works have been done about the link prediction in social networks. The goal of this paper is to…
Austin Polanco, M. E. J. Newman
Repurposing existing drugs to treat new diseases is a cost-effective alternative to de novo drug development, but there are millions of potential drug-disease combinations to be considered with only a small fraction being viable. In silico predictions of drug-disease associations can be invaluable for reducing the size…
Ahmad F. Al Musawi, Satyaki Roy, Preetam Ghosh
Link prediction algorithms in complex networks, such as social networks, biological networks, drug-drug interactions, communication networks, and so on, assign scores to predict potential links between two nodes. Link prediction (LP) enables researchers to learn unknown, new as well as future interactions among the…
Chengjun Zhang, Qi Li, Yi Lei, Ming Qian + 5 more
Link prediction is a hot issue in information filtering. Link prediction algorithms, based on local similarity indices, are widely used in many fields due to their high efficiency and high prediction accuracy. However, most existing link prediction algorithms are available for unweighted networks, and there are…
Aleta, Alberto, Tuninetti, Marta + 6 more
Link prediction algorithms can help to understand the structure and dynamics of complex systems, to reconstruct networks from incomplete data sets and to forecast future interactions in evolving networks. Available algorithms based on similarity between nodes are bounded by the limited amount of links present in these…
Yana Kashinskaya, Egor Samosvat, Akmal Artikov
We propose a link prediction algorithm that is based on springelectrical models. The idea to study these models came from the fact that spring-electrical models have been successfully used for networks visualization. A good network visualization usually implies that nodes similar in terms of network topology, e.g.…
Sho Tsugawa, Hiroyuki Ohsaki, Peter Csermely
Research on link prediction for social networks has been actively pursued. In link prediction for a given social network obtained from time-windowed observation, new link formation in the network is predicted from the topology of the obtained network. In contrast, recent advances in sensing technology have made it…
Baichuan Zhang, Sutanay Choudhury, Mohammad Al Hasan, Xia Ning + 3 more
'Khushbu Agarwal' 'Sumit Purohit' 'Paola Gabriela Pesntez Cabrera'] Link prediction, or predicting the likelihood of a link in a knowledge graph based on its existing state is a key research task. It differs from a traditional link prediction task in that the links in a knowledge graph are categorized into different…
Yue Hu, Svitlana Oleshko, Samuele Firmani, Zhaocheng Zhu + 7 more
Understanding complex interactions in biomedical networks is crucial for advancements in biomedicine. Traditional link prediction (LP) methods, using similarity metrics like Personalized PageRank, are limited in capturing the complexity of biological networks. Recently, representation-based learning techniques have…
Chao Wu, Peng Chen, Baiqiao Yin, Zijuan Lin + 4 more
'C. Zou' 'Chunwang Lui'] Abstract—Social networks exhibit a complex graph-like structure due to the uncertainty surrounding potential collaborations among participants. Machine learning algorithms possess generic outstanding performance in multiple real-world prediction tasks. However, whether machine learning…
Emma Rydholm, Tomas Bastys, Emma Svensson, Christos Kannas + 2 more
In this work, we present a new molecular de novo design approach which utilizes a knowledge graph encoding of chemical reactions, extracted from the publicly available USPTO (United States Patent and Trademark Office) dataset. Our proposed method can be used to expand the chemical space by performing forward synthesis…
Jeff Guo, Franziska Knuth, Christian Margreitter, Jon Paul Janet + 3 more
In this work, we present Link-INVENT as an extension to the existing de novo molecular design platform REINVENT. We provide illustrative examples on how Link-INVENT can be applied on fragment linking, scaffold hopping, and PROTACs design case studies where the desirable molecules should satisfy a combination of…
Rogini Runghen, Daniel B Stouffer, Giulio V Dalla Riva
Collecting network interaction data is difficult. Non-exhaustive sampling and complex hidden processes often result in an incomplete data set. Thus, identifying potentially present but unobserved interactions is crucial both in understanding the structure of large scale data, and in predicting how previously unseen…
Philipp-Maximilian Jacob, Alexei Lapkin
Is chemistry discoverable or can it only be invented? – this is the question of a computer scientist and a philosopher of science when looking at application of artificial intelligence methods for developing new chemical entities and new chemical transformations. This study confirms that, at least today, chemistry is…
Nihal Dadheech
In our research journey, we undertook a comprehensive exploration of protein-protein interaction (PPI) prediction, with a primary focus on unraveling the intricate web of interactions involving the SARS-CoV-2 virus. Our research endeavor encompassed a multi-faceted approach that seamlessly integrated data…