Name disambiguation from link data in a collaboration graph

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

doi:10.1109/asonam.2014.6921563

Cited by 26 publications

(29 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We will show in the experiment section that our method performs significantly better than this method in terms of both speed and accuracy. Note that, a preliminary version of our paper is already published as a short conference article [30].…”

Section: Related Workmentioning

confidence: 99%

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

Saha

Zhang

Hasan

2015

Soc. Netw. Anal. Min.

Self Cite

View full text Add to dashboard Cite

In a social community, multiple persons may share the same name, phone number or some other identifying attributes. This, along with other phenomena, such as name abbreviation, name misspelling, and human error leads to erroneous aggregation of records of multiple persons under a single reference. Such mistakes affect the performance of document retrieval, web search, database integration, and more importantly, improper attribution of credit (or blame). The task of entity disambiguation partitions the records belonging to multiple persons with the objective that each decomposed partition is composed of records of a unique person. Existing solutions to this task use either biographical attributes, or auxiliary features that are collected from external sources, such as Wikipedia. However, for many scenarios, such auxiliary features are not available, or they are costly to obtain. Besides, the attempt of collecting biographical or external data sustains the risk of privacy violation. In this work, we propose a method for solving entity disambiguation task from link information obtained from a collaboration network. Our method is non-intrusive of privacy as it uses only the time-stamped graph topology of an anonymized network. Experimental results on two real-life academic collaboration networks show that the proposed method has satisfactory performance.

show abstract

Section: Related Workmentioning

confidence: 99%

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

Saha

Zhang

Hasan

2015

Soc. Netw. Anal. Min.

Self Cite

View full text Add to dashboard Cite

show abstract

“…UUIL (Li et al 2018) is the basis of this work, but UUIL focus on the unsupervised learning, while SNNA aims to incorporate few annotations to improve the alignment performance. Recently several semi-supervised methods are proposed to incorporate the unlabeled data to capture the inner data distribution (Zafarani, Tang, and Liu 2015;Korula and Lattanzi 2014;Zhang et al 2015;Tan et al 2014;Liu et al 2016;Zhang, Saha, and Al Hasan ). Korula et al (Korula and Lattanzi 2014) introduced the label propagation, a popular semi-supervised model, to perform UIL task according to neighborhood-based network features.…”

Section: Related Workmentioning

confidence: 99%

Adversarial Learning for Weakly-Supervised Social Network Alignment

Wang

et al. 2019

AAAI

View full text Add to dashboard Cite

Nowadays, it is common for one natural person to join multiple social networks to enjoy different kinds of services. Linking identical users across multiple social networks, also known as social network alignment, is an important problem of great research challenges. Existing methods usually link social identities on the pairwise sample level, which may lead to undesirable performance when the number of available annotations is limited. Motivated by the isomorphism information, in this paper we consider all the identities in a social network as a whole and perform social network alignment from the distribution level. The insight is that we aim to learn a projection function to not only minimize the distance between the distributions of user identities in two social networks, but also incorporate the available annotations as the learning guidance. We propose three models SNNAu, SNNAb and SNNAo to learn the projection function under the weakly-supervised adversarial learning framework. Empirically, we evaluate the proposed models over multiple datasets, and the results demonstrate the superiority of our proposals.

show abstract

“…For each layer, there is an edge between two researchers if they coauthored at least one paper in the 5-year interval. For every edge in each layer, we adopt the temporal collaboration strength as the edge weight [51], [52]. Notably, while Prof. Leskovec and Prof. Ng both were members of the same department, there is no record of coauthorship between them on ArnetMiner.…”

Section: A Dataset Descriptionsmentioning

confidence: 99%

Multilayer Spectral Graph Clustering via Convex Layer Aggregation: Theory and Algorithms

Chen

Hero

2017

IEEE Trans. on Signal and Inf. Process. over Networks

View full text Add to dashboard Cite

Abstract-Multilayer graphs are commonly used for representing different relations between entities and handing heterogeneous data processing tasks. Non-standard multilayer graph clustering methods are needed for assigning clusters to a common multilayer node set and for combining information from each layer. This paper presents a multilayer spectral graph clustering (SGC) framework that performs convex layer aggregation. Under a multilayer signal plus noise model, we provide a phase transition analysis of clustering reliability. Moreover, we use the phase transition criterion to propose a multilayer iterative model order selection algorithm (MIMOSA) for multilayer SGC, which features automated cluster assignment and layer weight adaptation, and provides statistical clustering reliability guarantees. Numerical simulations on synthetic multilayer graphs verify the phase transition analysis, and experiments on realworld multilayer graphs show that MIMOSA is competitive or better than other clustering methods.

show abstract

Name disambiguation from link data in a collaboration graph

Cited by 26 publications

References 12 publications

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

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

Adversarial Learning for Weakly-Supervised Social Network Alignment

Multilayer Spectral Graph Clustering via Convex Layer Aggregation: Theory and Algorithms

Contact Info

Product

Resources

About