Name disambiguation from link data in a collaboration graph using temporal and topological features

Saha, Tanay Kumar; Zhang, Baichuan; Hasan, Mohammad Al

doi:10.1007/s13278-015-0249-1

Cited by 22 publications

(27 citation statements)

References 26 publications

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“…The research by Saha et al [16], and Hermansson et al [17] is most closely related to ours. These papers also only use topological information of the network for NDA.…”

Section: Related Worksupporting

confidence: 89%

FONDUE: A Framework for Node Disambiguation and Deduplication Using Network Embeddings

et al. 2021

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Data often have a relational nature that is most easily expressed in a network form, with its main components consisting of nodes that represent real objects and links that signify the relations between these objects. Modeling networks is useful for many purposes, but the efficacy of downstream tasks is often hampered by data quality issues related to their construction. In many constructed networks, ambiguity may arise when a node corresponds to multiple concepts. Similarly, a single entity can be mistakenly represented by several different nodes. In this paper, we formalize both the node disambiguation (NDA) and node deduplication (NDD) tasks to resolve these data quality issues. We then introduce FONDUE, a framework for utilizing network embedding methods for data-driven disambiguation and deduplication of nodes. Given an undirected and unweighted network, FONDUE-NDA identifies nodes that appear to correspond to multiple entities for subsequent splitting and suggests how to split them (node disambiguation), whereas FONDUE-NDD identifies nodes that appear to correspond to same entity for merging (node deduplication), using only the network topology. From controlled experiments on benchmark networks, we find that FONDUE-NDA is substantially and consistently more accurate with lower computational cost in identifying ambiguous nodes, and that FONDUE-NDD is a competitive alternative for node deduplication, when compared to state-of-the-art alternatives.

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“…The research by Saha et al [16], and Hermansson et al [17] is most closely related to ours. These papers also only use topological information of the network for NDA.…”

Section: Related Worksupporting

confidence: 89%

FONDUE: A Framework for Node Disambiguation and Deduplication Using Network Embeddings

et al. 2021

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show abstract

“…Moreover, ND does not assume the availability of a list of known unambiguous entity identifiers, such that an important part of the challenge is to identify which nodes are ambiguous in the first place. 1 The research by (Hermansson et al, 2013, Saha et al, 2015) is most closely related to ours. These papers also only use topological information of the network for ND.…”

Section: C) Related Worksupporting

confidence: 57%

“…as the sum of all the edges within cluster C i , W (C i , C i ) the sum of the for all the edges between cluster C i and the rest of the network C i , and k being the number of clusters in the graph. While (Hermansson et al, 2013) also worked on identitying nodes based on topological features, their method (which is not publicly available) performed worse in all the cases when compared to (Saha et al, 2015), so we only chose the latter as a competing baseline.…”

Section: Quantitative Evaluation Of Node Identificationmentioning

confidence: 99%

“…Ideally, this probability value is 1, which means our method has successfully identified ambiguous nodes 100% of the time, and the baseline value is 0.5, where the ambgiuous and nonambiguous nodes are indistinguishable. This accuracy measure has been used in other works in this field, including (Saha et al, 2015), which makes it easier to compare to their work. Evaluation pipeline.…”

Section: Connected-component Score (Cc)mentioning

confidence: 99%

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FONDUE: Framework for Node Disambiguation Using Network Embeddings

Mel

Kang

Lijffijt

et al. 2020

2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA)

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Real-world data often presents itself in the form of a network. Examples include social networks, citation networks, biological networks, and knowledge graphs. In their simplest form, networks represent real-life entities (e.g. people, papers, proteins, concepts) as nodes, and describe them in terms of their relations with other entities by means of edges between these nodes. This can be valuable for a range of purposes from the study of information diffusion to bibliographic analysis, bioinformatics research, and question-answering. The quality of networks is often problematic though, affecting downstream tasks. This paper focuses on the common problem where a node in the network in fact corresponds to multiple real-life entities. In particular, we introduce FONDUE, an algorithm based on network embedding for node disambiguation. Given a network, FONDUE identifies nodes that correspond to multiple entities, for subsequent splitting. Extensive experiments on fourteen benchmark datasets demonstrate that FONDUE is substantially and uniformly more accurate for ambiguous node identification compared to the existing state-of-the-art with a lower computational cost, while less optimal for determining the best way to split ambiguous nodes.

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“…With the ubiquity of cloud computing, outsourcing is a major trend. A graph is widely used, such as the collaboration graph , the network graph , and chemical molecular graph . The graphs may also increasingly be outsourced to the cloud.…”

Section: Introductionmentioning

confidence: 99%

Privacy-preserving ranked neighbor query over encrypted graph data in the cloud

Zhu

Xie

2016

Security Comm. Networks

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As cloud computing becomes prevalent, many data owners outsource their large number of graph data into the cloud for cost savings. The cloud may not be fully trusted and usually likes to learn owners' data. To protect the security of graph data, sensitive data have to be stored in an encrypted form. Neighbor queries are the most essential operations, many other operations can be built based on the neighbor queries. Ranked neighbor queries can make the queries very flexible, a query user can get ranked search results. When the graph data are encrypted, the operations over graph data are very inconvenient. Ranked neighbor queries over encrypted graph data are difficult to directly achieve. In this paper, we propose a solution to perform privacy‐preserving ranked neighbor query over encrypted graph data in the cloud, which is named as PRNQ. Our work utilizes ranking function and encryption mechanism to perform ranked neighbor query. The query users can obtain the most wanted search results by this solution. Our paper proposes a design scheme for the weighted directed graph and proves the security and correctness. Experimental results with a real graph data set demonstrate the validity and efficiency of our scheme. Copyright © 2016 John Wiley & Sons, Ltd.

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Name disambiguation from link data in a collaboration graph using temporal and topological features

Cited by 22 publications

References 26 publications

FONDUE: A Framework for Node Disambiguation and Deduplication Using Network Embeddings

FONDUE: A Framework for Node Disambiguation and Deduplication Using Network Embeddings

FONDUE: Framework for Node Disambiguation Using Network Embeddings

Privacy-preserving ranked neighbor query over encrypted graph data in the cloud

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