FLIP: Active Learning for Relational Network Classification

Saha, Tanwistha; Rangwala, Huzefa; Domeniconi, Carlotta

doi:10.1007/978-3-662-44845-8_1

Cited by 5 publications

(4 citation statements)

References 26 publications

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“…We evaluated our method on various learning tasks over three benchmark dataset collections, which include networked data for multiclass learning with features [40] and without features [39], and multilabel learning [34]. The datasets include diverse networks such as social networks, citation and co-authorship graphs, product and item networks, and hyperlink 0 0.2 0.4 0.6 0.…”

Section: Methodsmentioning

confidence: 99%

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Semi-Supervised Learning with Competitive Infection Models

Rosenfeld,

Globerson

2017

Preprint

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The goal in semi-supervised learning is to effectively combine labeled and unlabeled data. One way to do this is by encouraging smoothness across edges in a graph whose nodes correspond to input examples. In many graphbased methods, labels can be thought of as propagating over the graph, where the underlying propagation mechanism is based on random walks or on averaging dynamics. While theoretically elegant, these dynamics suffer from several drawbacks which can hurt predictive performance.Our goal in this work is to explore alternative mechanisms for propagating labels. In particular, we propose a method based on dynamic infection processes, where unlabeled nodes can be "infected" with the label of their already infected neighbors. Our algorithm is efficient and scalable, and an analysis of the underlying optimization objective reveals a surprising relation to other Laplacian approaches. We conclude with a thorough set of experiments across multiple benchmarks and various learning settings.

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Section: Methodsmentioning

confidence: 99%

“…We evaluated our method on various learning tasks over three collections of benchmark datasets, which include network based datasets for multi-class learning with features [40], multi-class learning without features [39], and multi-label learning [34]. The following table provides some statistics.…”

Section: Datasetsmentioning

confidence: 99%

Semi-Supervised Learning with Competitive Infection Models

Rosenfeld,

Globerson

2017

Preprint

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“…They conclude that using relational attributes built on both descriptive attributes and labels often produces the best accuracy. Finally, collective classification has been recently investigated in combination with active learning (Bilgic et al, 2010;Rattigan et al, 2007;Kuwadekar and Neville, 2011;Saha et al, 2014), as well as semi-supervised and transductive learning (Xiang and Neville, 2008;Shi et al, 2011a;McDowell and Aha, 2012).…”

Section: Related Workmentioning

confidence: 99%

Collective regression for handling autocorrelation of network data in a transductive setting

2015

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Sensor networks, communication and financial networks, web and social networks are becoming increasingly important in our day-to-day life. They contain entities which may interact with one another. These interactions are often characterized by a form of autocorrelation, where the value of an attribute at a given entity depends on the values at the entities it is interacting with. In this situation, the collective inference paradigm offers a unique opportunity to improve the performance of predictive models on network data, as interacting instances are labeled simultaneously by dealing with autocorrelation. Several recent works have shown that collective inference is a powerful paradigm, but it is mainly developed with a fully-labeled training network. In contrast, while it may be cheap to acquire the network topology, it may be costly to acquire node labels for training. In this paper, we examine how to explicitly consider autocorrelation when performing regression inference within network data. In particular, we study the transduction of collective regression when a sparsely labeled network is a common situation. We present an algorithm, called CORENA (COllective REgression in Network dAta), to assign a numeric label to each instance in the network. In particular, we iteratively augment the representation of each instance with instances sharing correlated representations across the network. In this way, the proposed learning model is able to capture autocorrelations of labels over a group of related instances and feed-back the more reliable labels predicted by the transduction in the labeled network. Empirical studies demonstrate that the proposed approach can boost regression performances in several spatial and social tasks

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“…LBC has been actively studied for over a decade and continues to attract significant interest in the machine learning [Bilgic et al 2010;Wang et al 2011;Saha et al 2014], data mining [Menon and Elkan 2010;Namata et al 2011;Jacob et al 2014], and knowledge management communities [Shi et al 2011;Kong et al 2012;Pfeiffer III et al 2014a]. Despite the additional complexity of inference, recent work has used CI much more frequently than RI for two reasons.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2016

ACM Trans. Knowl. Discov. Data

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Many analysis tasks involve linked nodes, such as people connected by friendship links. Research on linkbased classification (LBC) has studied how to leverage these connections to improve classification accuracy. Most such prior research has assumed the provision of a densely labeled training network. Instead, this article studies the common and challenging case when LBC must use a single sparsely labeled network for both learning and inference, a case where existing methods often yield poor accuracy. To address this challenge, we introduce a novel method that enables prediction via "neighbor attributes," which were briefly considered by early LBC work but then abandoned due to perceived problems. We then explain, using both extensive experiments and loss decomposition analysis, how using neighbor attributes often significantly improves accuracy. We further show that using appropriate semi-supervised learning (SSL) is essential to obtaining the best accuracy in this domain and that the gains of neighbor attributes remain across a range of SSL choices and data conditions. Finally, given the challenges of label sparsity for LBC and the impact of neighbor attributes, we show that multiple previous studies must be reconsidered , including studies regarding the best model features, the impact of noisy attributes, and strategies for active learning.

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FLIP: Active Learning for Relational Network Classification

Cited by 5 publications

References 26 publications

Semi-Supervised Learning with Competitive Infection Models

Semi-Supervised Learning with Competitive Infection Models

Collective regression for handling autocorrelation of network data in a transductive setting

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