8,799 research outputs found
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
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