A unified framework for link prediction based on non-negative matrix factorization with coupling multivariate information.

Solving the Credit Assignment Problem: The interaction of Explicit and Implicit learning with Internal and External State Information Wai-Tat Fu (wfu@cmu.edu) Human Factors Division and Beckman Institute University of Illinois at Urbana-Champaign 1 Airport Road, Savoy, IL 61874, USA John R. Anderson (ja+@cmu.edu) Department of Psychology Carnegie Mellon University 5000 Forbes Avenue Pittsburgh, PA 15213 USA recognition of external state information (signs on the walls). Indeed, many have argued that real-world skills often involve the interplay between cognition (internal), perception, and action (external) that the understanding of these interactive skills requires careful study of how internal (memory) and external information (cues in the environment) are processed in the learning processes (Ballard, 1997; Fu & Gray, 2000; 2004; Gray & Fu, 2004; Larkin, 1989; Gray, Sims, Fu, & Schoelles, in press). The navigation problem above is an example of one of the most difficult situations in skill learning: when the learner has to perform a sequence of actions but only gets feedback on their success at the end of the sequence (e.g., when the destination is reached). This creates a credit-assignment problem, in which the learner has to assign credits to earlier actions that are responsible for eventual success. When actions are interdependent, either memory of previous actions or recognition of the correct problem state in the external environment is required to properly assign credits to the appropriate actions. In this article, we present results from an experiment in which we study how people learn to solve the credit-assignment problem in a simple but challenging example of such a situation. Our focus is on the recent proposal that humans exhibit two distinct learning processes and we apply it to learning of action sequences with delayed feedback: an explicit process (with awareness) that requires memory for actions and outcomes, and an implicit process (without awareness) that does not require such memory. We will first review research in some related areas that informed the design of our experiment. Abstract In most problem-solving activities, feedback is received at the end of an action sequence. This creates a credit-assignment problem where the learner must associate the feedback with earlier actions, and the interdependencies of actions require the learner to either remember past choices of actions (internal state information) or rely on external cues in the environment (external state information) to select the right actions. We investigated the nature of explicit and implicit learning processes in the credit-assignment problem using a probabilistic sequential choice task with and without external state information. We found that when explicit memory encoding was dominant, subjects were faster to select the better option in their first choices than in the last choices; when implicit reinforcement learning was dominant subjects were faster to select the better option in their last choices than in their first choices. However, implicit reinforcement learning was only successful when distinct external state information was available. The results suggest the nature of learning in credit assignment: an explicit memory encoding process that keeps track of internal state information and a reinforcement-learning process that uses state information to propagate reinforcement backwards to previous choices. However, the implicit reinforcement learning process is effective only when the valences can be attributed to the appropriate states in the system – either internally generated states in the cognitive system or externally presented stimuli in the environment. Introduction Consider a person navigating in a large office building. The person has to decide when to turn left or right at various hallway intersections. The sequence of decisions is interdependent – e.g., turning left at a particular hallway intersection will affect the decisions at the next intersections. The person may therefore need to keep track of previous actions to inform what actions to take in the future. In reality, memory of previous actions (internal state information) may not be necessary as people can explicitly seek information in the environment (external state information) to know where one is located or which direction to go to reach a destination (Fu & Gray, 2006). Learning to navigate is therefore likely to involve both the retention of internal state information (memory) and the Explicit and Implicit Learning Probability Learning and Classification There have been numerous studies on the learning of the probabilistic relationship between choices and their consequences. The simplest situation is the probability- learning experiment in which subjects guess which of the alternatives occurs and then receives feedback on their guesses (e.g., Estes, 1964). One robust finding is that subjects often “probability match”; that is, they will choose a particular alternative with the same probability that it is reinforced (e.g., Friedman et al., 1964). This leads many to propose that probability matching is the result of an implicit

Link prediction based on bipartite networks can not only mine hidden relationships between different types of nodes, but also reveal the inherent law of network evolution. Existing bipartite network link prediction is mainly based on the global structure that cannot analyze the role of the local structure in link prediction. To tackle this problem, this paper proposes a deep link-prediction (DLP) method by leveraging the local structure of bipartite networks. The method first extracts the local structure between target nodes and observes structural information between nodes from a local perspective. Then, representation learning of the local structure is performed on the basis of the graph neural network to extract latent features between target nodes. Lastly, a deep-link prediction model is trained on the basis of latent features between target nodes to achieve link prediction. Experimental results on five datasets showed that DLP achieved significant improvement over existing state-of-the-art link prediction methods. In addition, this paper analyzes the relationship between local structure and link prediction, confirming the effectiveness of a local structure in link prediction.

Link prediction is a common problem in network science that cuts across many disciplines. The goal is to forecast the appearance of new links or to find links missing in the network. Typical methods for link prediction use the topology of the network to predict the most likely future or missing connections between a pair of nodes. However, network evolution is often mediated by higher-order structures involving more than pairs of nodes; for example, cliques on three nodes (also called triangles) are key to the structure of social networks, but the standard link prediction framework does not directly predict these structures. To address this gap, in recent work, we propose a new link prediction task called “pairwise link prediction” that directly targets the prediction of new triangles, where one is tasked with finding which nodes are most likely to form a triangle with a given edge. We extend this work in this manuscript, and we evaluate a variety of natural extensions to link prediction methods including neighborhood and PageRank-based methods. A key difference from our previous work is the definition of the neighborhood of an edge, which has a surprisingly large impact on the empirical performance. Our experiments on a variety of networks show that diffusion-based methods are less sensitive to the type of graphs used and more consistent in their results. We also show how our pairwise link prediction framework can be used to get better predictions within the context of standard link prediction evaluation.

In Advanced Driving Assistance Systems (ADASs), monitoring the driver's cognitive status during driving is considered as an important issue. Because, most of the accidents in the automotive sector occur due to the driver's misinterpretation or lack of sufficient information regarding the situation. In order to prevent these accidents, current ADASs include lane departure warning systems, vehicle detection systems, advanced cruise control systems, etc. In a particular driving scenario, the amount of information available to the driver regarding a situation can be judged by monitoring the driver's gaze (internal information) and distributions corresponding to the forward traffic (external information). Therefore, to provide sufficient information to the driver regarding a driving scenario it is essential to integrate the internal and external information which is lacking in the current ADASs. In this paper, we use 3D pose estimate algorithm (POSIT) to estimate driver's attention area. In order to estimate the distributions corresponding to the forward traffic we employ Bottom-up Saliency map. To integrate the internal and external information we use conditional mutual information.

Brain potentials related to self-generated and external information used for performance monitoring

Introduction Network representation can give a holistic view of relationships for biomedical entities through network topology. Link prediction estimates the probability of link formation between the pair of unconnected nodes. In the drug discovery process, the link prediction method not only enables the detection of connectivity patterns but also predicts the effects of one biomedical entity to multiple entities simultaneously and vice versa, which is useful for many applications. Areas covered The authors provide a comprehensive overview of network link prediction in drug discovery. Link prediction methodologies such as similarity-based approaches, embedding-based approaches, probabilistic model-based approaches, and preprocessing methods are summarized with examples. In addition to describing their properties and limitations, the authors discuss the applications of link prediction in drug discovery based on the relationship between biomedical concepts. Expert opinion Link prediction is a powerful method to infer the existence of novel relationships in drug discovery. However, link prediction has been hampered by the sparsity of data and the lack of negative links in biomedical networks. With preprocessing to balance positive and negative samples and the collection of more data, the authors believe it is possible to develop more reliable link prediction methods that can become invaluable tools for successful drug discovery.

Link prediction is widely used in network analysis to identify future links between nodes. Link prediction has an important place in terms of being applicable to many real-world networks with dynamic structure. Networks with dynamic structure, such as social networks, scientific collaboration networks and metabolic networks, are networks in which link prediction studies are performed. In addition, it is seen that there are few studies showing the feasibility of link prediction by creating networks from different areas. In this study, in order to show the applicability of link prediction processes in different fields link prediction was made by applying traditional link prediction methods in the networks formed from the data of football competitions played after the groups between the years 2004–2017 in the UEFA European League. The AUC metric was used to measure the success of forecasting. The results show that link prediction methods can be used in sports networks.

Improving local clustering based top-L link prediction methods via asymmetric link clustering information

Improving embedding-based link prediction performance using clustering

Link prediction aims to find the missing link in current networks or estimate the likelihood the link will appear in the future. In many real-world scenarios, networks can be massive and drastically evolving. Many recent works concentrated on how to solve the procession of massive data, which cares less about the effects of the temporal information. In some dynamic networks, the edge that appears a long time ago will have fewer effects on the appearance of edges in the future. The accuracy of link prediction in dynamic networks can be further improved by adding temporal information in prediction measures. In this paper, we mainly explore the effects of the link temporal information in the graph stream link prediction. We design a graph stream-based framework to solve link prediction problems in dynamic networks which can provide a convenient platform to implement different link prediction methods for dynamic networks, and evaluate these methods on a fair basis. Then, we proposed the Time Decay-based Link Prediction (TDLP) method to improve the efficient of link prediction problem in the dynamic networks. TDLP extends the neighborhood-based measure with time decay functions in graph stream scenarios which can handle temporal information and massive data. Experiment results demonstrate that the accuracy of link prediction for dynamic networks can be efficiently improved in our TDLP method.

Many link prediction methods have been developed in past few decades in network science, and most of them are tailored for specific fields that lose generalities. Luckily, a recent work named Structural Perturbation Method (SPM) proposed a consistency index of network organization without priori knowledge, and it did not need to test those predicting methods first. Since demonstrating whether there is a link between two nodes is usually costly, we want to replace link confirmation with link prediction. In this paper, we use the SPM method to study the structure of US political blog network and analyze the link predictability at different network scales. The experimental results show that we can obtain basic intrinsic features of the network structure and can get ideal prediction results as long as 20% of the network structures.

The purpose of this paper is to design a mathematical model for a robot vision, that can express states of human's consciousness, and build a dynamical information processing system, that can change processing modes. Especially the author takes notice to a state transition between waking and sleep. A human being processes external information obtained from sensory organs and internal information accumulated within a brain flexibly and in parallel. The proposed mathematical consciousness model controls a ratio between external and internal information processing runs in parallel. In a waking mode, the external information is mainly processed. In a sleep mode, large proportion of processing is paused, or the internal information is mainly processed. The advantage of this system is that a resource of a computing machine is made effective use of. Because required processes are executed only when needed.

A heterogeneous information network, which is composed of various types of nodes and edges, has a complex structure and rich information content, and is widely used in social networks, academic networks, e-commerce, and other fields. Link prediction, as a key task to reveal the unobserved relationships in the network, is of great significance in heterogeneous information networks. This paper reviews the application of presentation-based learning methods in link prediction of heterogeneous information networks. This paper introduces the basic concepts of heterogeneous information networks, and the theoretical basis of representation learning, and discusses the specific application of the deep learning model in node embedding learning and link prediction in detail. The effectiveness and superiority of these methods on multiple real data sets are demonstrated by experimental verification.

Network embedding aims to learn distributed vector representations of nodes in a network. The problem of network embedding is fundamentally important. It plays crucial roles in many applications, such as node classification, link prediction, and so on. As the real-world networks are often sparse with few observed links, many recent works have utilized the local and global network structure proximity with shallow models for better network embedding. In reality, each node is usually associated with rich attributes. Some attributed network embedding models leveraged the node attributes in these shallow network embedding models to alleviate the data sparsity issue. Nevertheless, the underlying structure of the network is complex. What is more, the connection between the network structure and node attributes is also hidden. Thus, these previous shallow models fail to capture the nonlinear deep information embedded in the attributed network, resulting in the suboptimal embedding results. In this paper, we propose a deep attributed network embedding framework to capture the complex structure and attribute information. Specifically, we first adopt a personalized random walk-based model to capture the interaction between network structure and node attributes from various degrees of proximity. After that, we construct an enhanced matrix representation of the attributed network by summarizing the various degrees of proximity. Then, we design a deep neural network to exploit the nonlinear complex information in the enhanced matrix for network embedding. Thus, the proposed framework could capture the complex attributed network structure by preserving both the various degrees of network structure and node attributes in a unified framework. Finally, empirical experiments show the effectiveness of our proposed framework on a variety of network embedding-based tasks.

Matrix completion based collaborative filtering is considered scalable and effective for online service link prediction (e.g., movie recommendation) but does not meet the challenges of link prediction in ecological networks. A unique challenge of ecological networks is that the observed data are subject to systematic imperfect detection, due to the difficulty of accurate field sampling. In this work, we propose a new framework customized for ecological bipartite network link prediction. Our approach starts with incorporating the Poisson N-mixture model, a widely used framework in statistical ecology for modeling imperfect detection of a single species in field sampling. Despite its extensive use for single species analysis, this model has never been considered for link prediction between different species, perhaps because of the complex nature of both link prediction and N-mixture model inference. By judiciously combining the Poisson N-mixture model with a probabilistic nonnegative matrix factorization (NMF) model in latent space, we propose an intuitive statistical model for the problem of interest. We also offer a scalable and convergence-guaranteed optimization algorithm to handle the associated maximum likelihood identification problem. Experimental results on synthetic data and two real-world ecological networks data are employed to validate our proposed approach.