FutureRank: Ranking Scientific Articles by Predicting their Future PageRank

Sayyadi, Hassan; Getoor, Lise

doi:10.1137/1.9781611972795.46

Cited by 110 publications

(158 citation statements)

References 19 publications

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“…However, it is important for researchers to be able to find such recent articles because they can discover new research directions, solutions and approaches, and digest new work that is relevant to their current interests. As such, Sayyadi and Getoor [28] proposed "FutureRank" which computes the expected future PageRank, focusing on citation network of scholarly papers.…”

Section: Ranking Search Resultsmentioning

confidence: 99%

Scholarly paper recommendation via user's recent research interests

Sugiyama

Kan

2010

Proceedings of the 10th Annual Joint Conference on Digital Libraries

162

112

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We examine the effect of modeling a researcher's past works in recommending scholarly papers to the researcher. Our hypothesis is that an author's published works constitute a clean signal of the latent interests of a researcher. A key part of our model is to enhance the profile derived directly from past works with information coming from the past works' referenced papers as well as papers that cite the work. In our experiments, we differentiate between junior researchers that have only published one paper and senior researchers that have multiple publications. We show that filtering these sources of information is advantageous -when we additionally prune noisy citations, referenced papers and publication history, we achieve statistically significant higher levels of recommendation accuracy.

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Section: Ranking Search Resultsmentioning

confidence: 99%

Scholarly paper recommendation via user's recent research interests

Sugiyama

Kan

2010

Proceedings of the 10th Annual Joint Conference on Digital Libraries

162

112

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“…Beyond simple citations count, researchers have explored methods that analyze the structure of citation networks to identify important papers [31], [32] or predict which papers will be important in the future [33].…”

Section: Related Workmentioning

confidence: 99%

Identifying Transformative Scientific Research

Huang

Hsu²,

Lerman³

2013

2013 IEEE 13th International Conference on Data Mining

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Abstract-Transformative research refers to research that shifts or disrupts established scientific paradigms. Notable examples include the discovery of high-temperature superconductivity that disrupted the theory established 30 years ago. Identifying potential transformative research early and accurately is important for funding agencies to maximize the impact of their investments. It also helps scientists identify and focus their attention on promising emerging works. This paper presents a datadriven approach where citation patterns of scientific papers are analyzed to quantify how much a potential challenger idea shifts an established paradigm. The key idea is that transformative research creates an observable disruption in the structure of "information cascades," chains of references that can be traced back to the papers establishing some scientific paradigm. Such a disruption is visible soon after the challenger's introduction. We define a disruption score to quantify the disruption and develop an algorithm to compute it from a large citation network. Experimental results show that our approach can successfully identify transformative scientific papers that disrupt established paradigms in Physics and Computer Science, regardless of whether the challenger paradigm is an instant hit or a classic whose contribution is formally recognized with a Nobel Prize decades later.

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“…7.2, an approach which takes into account the peculiar properties of each dataset will give better results. Considering that in literature there are already a notable amount of ranking algorithms that are dataset specific, such as [18,19] for citation networks or Dissipative Heat Conductance [12] for strongly hierarchical datasets like taxonomies or geo-databases, DING has been designed to exploit better alternatives to LinkCount and EntityRank. One can also define its own algorithm using dataset-dependent ranking criteria.…”

Section: Semantic-dependent Entity Ranking Datasets On the Web Of Damentioning

confidence: 99%

“…We measure the Spearman's correlation [21] of the two local algorithms with GER using the full entity rank list of three datasets: DBpedia, Citesser and Geonames. The Spearman's correlation has already been employed in [18] to compare several Table 2 7 . While LLC performs slightly worse than LER, the Spearman's correlation indicates a strong correlation of the two algorithms with GER.…”

Section: Accuracy Of Local Entity Rankmentioning

confidence: 99%

Hierarchical Link Analysis for Ranking Web Data

Delbru

Toupikov

Catasta

et al. 2010

Lecture Notes in Computer Science

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Abstract. On the Web of Data, entities are often interconnected in a way similar to web documents. Previous works have shown how PageRank can be adapted to achieve entity ranking. In this paper, we propose to exploit locality on the Web of Data by taking a layered approach, similar to hierarchical PageRank approaches. We provide justifications for a two-layer model of the Web of Data, and introduce DING (Dataset Ranking) a novel ranking methodology based on this two-layer model. DING uses links between datasets to compute dataset ranks and combines the resulting values with semantic-dependent entity ranking strategies. We quantify the effectiveness of the approach with other link-based algorithms on large datasets coming from the Sindice search engine. The evaluation which includes a user study indicates that the resulting rank is better than the other approaches. Also, the resulting algorithm is shown to have desirable computational properties such as parallelisation.

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FutureRank: Ranking Scientific Articles by Predicting their Future PageRank

Cited by 110 publications

References 19 publications

Scholarly paper recommendation via user's recent research interests

Scholarly paper recommendation via user's recent research interests

Identifying Transformative Scientific Research

Hierarchical Link Analysis for Ranking Web Data

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