Link Prediction in Complex Networks: A Survey

Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods. We also introduce three typical applications: reconstruction of networks, evaluation of network evolving mechanism and classification of partially labelled networks. Finally, we introduce some applications and outline future challenges of link prediction algorithms.Comment: 44 pages, 5 figure

Temporal networks of face-to-face human interactions

The ever increasing adoption of mobile technologies and ubiquitous services allows to sense human behavior at unprecedented levels of details and scale. Wearable sensors are opening up a new window on human mobility and proximity at the finest resolution of face-to-face proximity. As a consequence, empirical data describing social and behavioral networks are acquiring a longitudinal dimension that brings forth new challenges for analysis and modeling. Here we review recent work on the representation and analysis of temporal networks of face-to-face human proximity, based on large-scale datasets collected in the context of the SocioPatterns collaboration. We show that the raw behavioral data can be studied at various levels of coarse-graining, which turn out to be complementary to one another, with each level exposing different features of the underlying system. We briefly review a generative model of temporal contact networks that reproduces some statistical observables. Then, we shift our focus from surface statistical features to dynamical processes on empirical temporal networks. We discuss how simple dynamical processes can be used as probes to expose important features of the interaction patterns, such as burstiness and causal constraints. We show that simulating dynamical processes on empirical temporal networks can unveil differences between datasets that would otherwise look statistically similar. Moreover, we argue that, due to the temporal heterogeneity of human dynamics, in order to investigate the temporal properties of spreading processes it may be necessary to abandon the notion of wall-clock time in favour of an intrinsic notion of time for each individual node, defined in terms of its activity level. We conclude highlighting several open research questions raised by the nature of the data at hand.Comment: Chapter of the book "Temporal Networks", Springer, 2013. Series: Understanding Complex Systems. Holme, Petter; Saram\"aki, Jari (Eds.

A Unified Community Detection, Visualization and Analysis method

Community detection in social graphs has attracted researchers' interest for a long time. With the widespread of social networks on the Internet it has recently become an important research domain. Most contributions focus upon the definition of algorithms for optimizing the so-called modularity function. In the first place interest was limited to unipartite graph inputs and partitioned community outputs. Recently bipartite graphs, directed graphs and overlapping communities have been investigated. Few contributions embrace at the same time the three types of nodes. In this paper we present a method which unifies commmunity detection for the three types of nodes and at the same time merges partitionned and overlapping communities. Moreover results are visualized in such a way that they can be analyzed and semantically interpreted. For validation we experiment this method on well known simple benchmarks. It is then applied to real data in three cases. In two examples of photos sets with tagged people we reveal social networks. A second type of application is of particularly interest. After applying our method to Human Brain Tractography Data provided by a team of neurologists, we produce clusters of white fibers in accordance with other well known clustering methods. Moreover our approach for visualizing overlapping clusters allows better understanding of the results by the neurologist team. These last results open up the possibility of applying community detection methods in other domains such as data analysis with original enhanced performances.Comment: Submitted to Advances in Complex System

Characterizing and Detecting Unrevealed Elements of Network Systems

This dissertation addresses the problem of discovering and characterizing unknown elements in network systems. Klir (1985) provides a general definition of a system as “... a set of some things and a relation among the things (p. 4). A system, where the `things\u27, i.e. nodes, are related through links is a network system (Klir, 1985). The nodes can represent a range of entities such as machines or people (Pearl, 2001; Wasserman & Faust, 1994). Likewise, links can represent abstract relationships such as causal influence or more visible ties such as roads (Pearl, 1988, pp. 50-51; Wasserman & Faust, 1994; Winston, 1994, p. 394). It is not uncommon to have incomplete knowledge of network systems due to either passive circumstances, e.g. limited resources to observe a network, active circumstances, e.g. intentional acts of concealment, or some combination of active and passive influences (McCormick & Owen, 2000, p. 175; National Research Council, 2005, pp. 7, 11). This research provides statistical and graph theoretic approaches for such situations, including those in which nodes are causally related (Geiger & Pearl, 1990, pp. 3, 10; Glymour, Scheines, Spirtes, & Kelly, 1987, pp. 75-86, 178183; Murphy, 1998; Verma & Pearl, 1991, pp. 257, 260, 264-265). A related aspect of this research is accuracy assessment. It is possible an analyst could fail to detect a network element, or be aware of network elements, but incorrectly conclude the associated network system structure (Borgatti, Carley, & Krackhardt, 2006). The possibilities require assessment of the accuracy of the observed and conjectured network systems, and this research provides a means to do so (Cavallo & Klir, 1979, p. 143; Kelly, 1957, p. 968)

그래프 학습시 콜드 스타트 문제 해결을 위한 멀티테스크 학습 전략

학위논문(석사) -- 서울대학교대학원 : 자연과학대학 협동과정 뇌과학전공, 2021.8. 장병탁.Data in real world can commonly be represented as graph data. From molecules, social networks, Internet, and even natural language can be represented as graph. Therefore, generating fine representations from the graph data is critical in machine learning. One of the common attributes of most real-world graphs is that graphs are dynamic and can eventually face the cold start problem. A fundamental question is how the new cold nodes acquire initial information in order to be adapted into the existing graph. Here we postulates the cold start problem as a fundamental issue in graph learning and propose a new learning setting, “Expanded Semi-supervised Learning.” In expanded semi-supervised learning we extend the original semi-supervised learning setting even to new cold nodes that are disconnected from the graph. To this end, we propose Cold- Expand model that classifies the cold nodes based on link prediction with mul- tiple goals to tackle. We experimentally prove that by adding additional goal to existing link prediction method, our method outperforms the baseline in both expanded semi-supervised link prediction (at most 24%) and node classifica- tion tasks (at most 15%). To the best of our knowledge this is the first study to address expansion of semi-supervised learning to unseen nodes.대다수의 실제하는 그래프 데이터는 시간에 따라 노드와 그 연결이 변화하고 필연 적으로 콜드스타트 문제를 직면하게 된다. 콜드스타트 문제를 해결하기 위해서는 새로 발생한 콜드 노드들이 기존 그래프에 포함되기 위해 어떻게 초기 정보를 얻을 수 있는지가 매우 중요하다. 본 논문에서는 그래프 학습에서의 콜드스타트 문제를 제기하고 새롭고 현실적인 학습 세팅인 ”확장된 준지도학습”을 제안한다. 확장된 준지도학습에서는 기존의 준지도 학습을 새로 소개되어 기존 그래프와의 연결성 이 없는 콜드 노드들에도 적용한다. 새로운 학습 세팅을 위해 논문에서는 콜드 노드들의 분류를 위해 링크 예측을 함께 멀티테스크 목적으로 갖는 ColdExpand 모델을 제시한다. 또한 실험적으로 우리 모델이 추가적인 목적을 모델에 더해줌 으로써 콜드 노드들의 링크 예측과 분류 문제에서 과거 모델들을 크게 상회하는 결과가 나옴을 증명한다.Chapter 1 Introduction 1 Chapter 2 Related Works 5 2.1 Semi-supervisedNodeClassification 5 2.2 LinkPrediction 6 2.3 ColdStartProblem 8 2.4 Multi-taskLearningStrategy 9 Chapter 3 Method 11 3.1 ProblemDefinition 11 3.2 ColdExpand: Semi-Supervised Graph Learning in Cold Start 12 3.2.1 LinkPredictionofColdNodes 12 3.2.2 NodeClassificationofColdNodes 13 Chapter 4 Experiment 17 4.1 Dataset 17 4.2 Baseline 18 4.2.1 Expanded Semi-supervised Link Prediction 18 4.2.2 Expanded Semi-supervised Node Classification 18 4.3 Results. 19 4.3.1 Results of Link Prediction on Cold Nodes 19 4.3.2 Results of Semi-supervised Node Classification on Cold Nodes. 20 4.3.3 Qualitative Results for Node Classification in Cold Start Environment 23 4.3.4 ColdExpand also Improves Conventional Semi-supervised NodeClassification 23 4.3.5 AblationStudy 25 Chapter 5 Discussion 28석