A hidden treasure? Evaluating and extending latent methods for link-based classification

Fleming, Aaron; McDowell, Luke K.; Markel, Zane

doi:10.1109/iri.2014.7051954

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…In recent work [Fleming et al 2014;, we evaluate many of the methods described above, including those of Tang et al, Shi et al, and Lin and Cohen. Compared to RCI, RI, and CI, we found that some of the the latent methods can be competitive when the network is densely labeled or when the attributes are not very informative but that when the network is sparsely labeled, RCI and/or RI generally provide the best accuracy, with some significant gains vs. the latent methods.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

confidence: 99%

Untitled

2016

ACM Trans. Knowl. Discov. Data

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“…An earlier version of this work was described by Fleming et al [5]. That earlier paper compared latent vs. non-latent methods and introduced the taxonomy of Section 6.…”

Section: Related Workmentioning

confidence: 99%

Evaluating and Extending Latent Methods for Link-Based Classification

McDowell

Fleming

Markel

2015

Formalisms for Reuse and Systems Integration

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Data describing networks such as social networks, citation graphs, hypertext systems, and communication networks is becoming increasingly common and important for analysis. Research on link-based classification studies methods to leverage connections in such networks to improve accuracy. Recently, a number of such methods have been proposed that first construct a set of latent features or links that summarize the network, then use this information for inference. Some work has claimed that such latent methods improve accuracy, but has not compared against the best non-latent methods. In response, this article provides the first substantial comparison between these two groups. Using six real datasets, a range of synthetic data, and multiple underlying models, we show that (non-latent) collective inference methods usually perform best, but that the dataset's label sparsity, attribute predictiveness, and link density can dramatically affect the performance trends. Inspired by these findings, we introduce three novel algorithms that combine a latent construction with a latent or non-latent method, and demonstrate that they can sometimes substantially increase accuracy.

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A hidden treasure? Evaluating and extending latent methods for link-based classification

Cited by 2 publications

References 19 publications

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Evaluating and Extending Latent Methods for Link-Based Classification

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