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Simrank++

Ioannis Antonellis, Hector Garcia Molina, Chi Chao Chang
2008 Proceedings of the VLDB Endowment  
We argue that Simrank fails to properly identify query similarities in our application, and we present two enhanced versions of Simrank: one that exploits weights on click graph edges and another that  ...  Given a query q, we first consider Simrank [2] as a way to identify queries similar to q, i.e., queries whose ads a user may be interested in.  ...  We plan to experiment with these schemes in other domains, including collaborative filtering.  ... 
doi:10.14778/1453856.1453903 fatcat:pmn2lfdtnfdn3p3jy7xvnfcyi4

Simrank++

Ioannis Antonellis, Hector Garcia-Molina, Chi-Chao Chang
2008 Proceeding of the 17th international conference on World Wide Web - WWW '08  
We argue that Simrank fails to properly identify query similarities in our application, and we present two enhanced versions of Simrank: one that exploits weights on click graph edges and another that  ...  Given a query q, we first consider Simrank [2] as a way to identify queries similar to q, i.e., queries whose ads a user may be interested in.  ...  We plan to experiment with these schemes in other domains, including collaborative filtering.  ... 
doi:10.1145/1367497.1367714 dblp:conf/www/AntonellisGC08 fatcat:v3foozuv2zhfjiqjkuw3ap7t7i

Merging Word Senses

Sumit Bhagwani, Shrutiranjan Satapathy, Harish Karnick
2013 Workshop on Graph-based Methods for Natural Language Processing  
We seed our framework with sense similarities of all the word-sense pairs, learnt using supervision on humanlabelled sense clusterings.  ...  Finally we discuss our method to derive coarse sense inventories at arbitrary granularities and show that the coarse-grained sense inventory obtained significantly boosts the disambiguation of nouns on  ...  . • We scale the posterior probabilities obtained to range between [0, C] by linear scaling, where C is the SimRank decay parameter. 4 Seeding SimRank with supervision Outline We learn semantic similarity  ... 
dblp:conf/textgraphs/BhagwaniSK13 fatcat:yt53f34hevgbbiqflvztcqbhoq

Simrank++: Query rewriting through link analysis of the click graph [article]

Ioannis Antonellis, Hector Garcia-Molina, Chi-Chao Chang
2007 arXiv   pre-print
We argue that Simrank fails to properly identify query similarities in our application, and we present two enhanced version of Simrank: one that exploits weights on click graph edges and another that exploits  ...  Given a query q, we first consider Simrank as a way to identify queries similar to q, i.e., queries whose ads a user may be interested in.  ...  Acknowledgements We thank Kevin Lang for providing us his code from [1] and for helping us with the subgraph extraction procedure. We also thank Yahoo!  ... 
arXiv:0712.0499v1 fatcat:tx7ml7ywcjaxdji2rey6vvdepy

A Survey of Data Mining Techniques on Information Networks

Sadhana Kodali, Madhavi Dabbiru, B Thirumala Rao
2018 International Journal of Engineering & Technology  
The essence of SimFusion is similar to that of SimRank but with less time complexity.  ...  The essence of SimFusion is similar to that of SimRank but with less time complexity.  ... 
doi:10.14419/ijet.v7i2.6.11267 fatcat:zavu7rli4ja2ne3nj6wiz4wxhi

Link-Based Similarity Search to Fight Web Spam

András A. Benczúr, Károly Csalogány, Tamás Sarlós
2006 Adversarial Information Retrieval on the Web  
for propagation, we form classifiers by investigating similarity top lists of an unknown page along various measures such as co-citation, companion, nearest neighbors in low dimensional projections and SimRank  ...  of further SimRank variants.  ...  Moreover, akin to [32] it needs to be investigated whether the accuracy of content based spam classifiers can be boosted by incorporating estimates assigned to similar nodes.  ... 
dblp:conf/airweb/BenczurCS06 fatcat:zfqbt7hsmfcjxjmsgfpnmedf5q

Efficient Pairwise Penetrating-rank Similarity Retrieval

Weiren Yu, Julie McCann, Chengyuan Zhang
2019 ACM Transactions on the Web  
paper, we consider the optimization techniques for P-Rank search that encompasses its accuracy, stability and computational efficiency. (1) The accuracy estimation is provided for P-Rank iterations, with  ...  matrix-based algorithms (DE P-Rank and UN P-Rank) 1 that can substantially speed up the computation of P-Rank from O(kn 3 ) to O(υn 2 + υ 6 ) for digraphs, and to O(υn 2 ) for undirected graphs (Section 5) with  ...  P-Rank provides a comprehensive way of jointly considering both in-and out-link relationships in a network with semantic completeness.  ... 
doi:10.1145/3368616 fatcat:wercjvjztra4vbcbk4n77scfcy

Finding Experts by Link Prediction in Co-authorship Networks

Milen Pavlov, Ryutaro Ichise
2007 Personal Identification and Collaborations: Knowledge Mediation and Extraction  
It could also help with choosing vocabulary for expert description, since link predictors contain implicit information about which structural attributes of the network are important with respect to the  ...  AdaBoost can be used with any of the previously mentioned predictors, although it generally achieves a better performance boost when applied with simpler predictors (e.g., decision stump).  ...  As a result, an expert in a particular field might find himself uncertain as to who to collaborate with. Experts' semantic descriptions might be very helpful here.  ... 
dblp:conf/fews/PavlovI07 fatcat:r7rwkwktzzcsrivgiu5rftr4gm

Item Tagging for Information Retrieval: A Tripartite Graph Neural Network based Approach [article]

Kelong Mao, Xi Xiao, Jieming Zhu, Biao Lu, Ruiming Tang, Xiuqiang He
2020 arXiv   pre-print
This formulation results in a TagGNN model that utilizes heterogeneous graph neural networks with multiple types of nodes and edges.  ...  Tagging has been recognized as a successful practice to boost relevance matching for information retrieval (IR), especially when items lack rich textual descriptions.  ...  Thus, comparatively speaking, TagGNNs get the biggest percentages of boost from the query information, demonstrating that TagGNNs can utilize queries better than other baselines. • Graph-based SimRank-QI  ... 
arXiv:2008.11567v1 fatcat:b3sxjo7fpnbz7m3p32mmry7r3a

Analysis of Music Retrieval Based on Emotional Tags Environment

Nuan Bao, Zhao kaifa
2022 Journal of Environmental and Public Health  
By modelling user emotional tags and music, a bipartite graph with emotional tags and music as nodes is first created.  ...  The tags and semantic similarity between the music are then calculated using the T_SimRank algorithm, and the popularity of the music is calculated using the T_PageRank algorithm.  ...  When T SimRank calculates the similarity, it will have an advantage for songs with a relatively small number of tags.  ... 
doi:10.1155/2022/4670963 pmid:36017242 pmcid:PMC9398802 fatcat:5k54cvjfa5b4jaadchny5c3amm

An Experimental Study on Unsupervised Graph-based Word Sense Disambiguation [chapter]

George Tsatsaronis, Iraklis Varlamis, Kjetil Nørvåg
2010 Lecture Notes in Computer Science  
The graph is expanded by adding semantic edges and nodes from a thesaurus.  ...  Among the latest state of the art methods, those that use semantic graphs reported the best results.  ...  This means that in a possible ensemble, the combination of SAN with PR or HITS could boost the overall performance.  ... 
doi:10.1007/978-3-642-12116-6_16 fatcat:d5nwat7k2bcf5hxfr3seousix4

Scalable mining and link analysis across multiple database relations

Xiaoxin Yin
2008 SIGKDD Explorations  
Since information is widely spread across multiple relations, the most crucial and common challenge in multi-relational data mining is how to utilize the relational information linked with each object.  ...  A new graduate student with very little research experiences, as I was five years ago, has become a Ph.D. candidate capable of performing research independently on newly emerging data mining issues.  ...  Thus we may use boosting technique to improve its accuracy.  ... 
doi:10.1145/1540276.1540283 fatcat:xr3vnpi33vfybiecexzjhz66ua

PReP

Yu Shi, Po-Wei Chan, Honglei Zhuang, Huan Gui, Jiawei Han
2017 Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '17  
PathSim w (s) ≔ ∑ t w t • • SimRank. We adopt SimRank [8] with meta-path constraints.  ...  We tune the decay factor C in the baseline measure, SimRank, to have the best performance with C = 0.5 for both SimRank-Mean and SimRank-SD on Facebook, and C = 0.8 for SimRank-Mean, C = 0.7 for SimRank-SD  ... 
doi:10.1145/3097983.3097990 pmid:30221026 pmcid:PMC6135112 dblp:conf/kdd/ShiCZG017 fatcat:oxrhxrlwc5b35k2l54ddzl76ve

A Survey on Mining and Analysis of Uncertain Graphs [article]

Suman Banerjee
2021 arXiv   pre-print
They also showed that under the probabilistic semantic the computation of 'Support' is #P-Complete. Hence, their solutions are approximate in nature with accuracy guarantee.  ...  Definition 2 (Possible World Semantic) By this model, an uncertain graph is represented as a probability distribution over 2 m number of deterministic graphs by keeping an edge with probability P (e) and  ... 
arXiv:2106.07837v1 fatcat:f3wuv5rhqnhk5lbhdmzl7f2bgm

Locating Faulty Methods with a Mixed RNN and Attention Model [article]

Shouliang Yang, Junming Cao, Hushuang Zeng, Beijun Shen, Hao Zhong
2021 arXiv   pre-print
We conduct a preliminary study to explore its challenges, and identify three problems: the semantic gap problem, the representation sparseness problem, and the single revision problem.  ...  IR-based fault localization approaches achieves promising results when locating faulty files by comparing a bug report with source code.  ...  M rel (S) includes two types of methods: M sim (S) -a set of methods with high SimRank similarity scores for S, and M call (S) -a set of methods that S calls.  ... 
arXiv:2103.10675v1 fatcat:7bxqhh3u7veunmeltrton775ei
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