國立虎尾科技大學 |

Collective Multi-Relational Network Mining.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Collective Multi-Relational Network Mining./
作者:	Fakhraei, Seyed Shobeir.
面頁冊數:	1 online resource (149 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.
Contained By:	Dissertation Abstracts International79-03B(E).
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9780355302035

Collective Multi-Relational Network Mining.
Fakhraei, Seyed Shobeir.

Collective Multi-Relational Network Mining. - 1 online resource (149 pages)

Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.

Thesis (Ph.D.)--University of Maryland, College Park, 2017.

Includes bibliographical references

Our world is becoming increasingly interconnected, and the study of networks and graphs are becoming more important than ever. Domains such as biological and pharmaceutical networks, online social networks, the World Wide Web, recommender systems, and scholarly networks are just a few examples that include explicit or implicit network structures. Most networks are formed between different types of nodes and contain different types of links. Leveraging these multi-relational and heterogeneous structures is an important factor in developing better models for these real-world networks. Another important aspect of developing models for network data to make predictions about entities such as nodes or links, is the connections between such entities. These connections invalidate the i.i.d. assumptions about the data in most traditional machine learning methods. Hence, unlike models for non-network data where predictions about entities are made independently of each other, the inter-connectivity of the entities in networks should cause the inferred information about one entity to change the models belief about other related entities.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355302035Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Collective Multi-Relational Network Mining.
LDR:03740ntm a2200361Ki 4500 001 916796
005 20180928111501.5
006 m o u
007 cr mn||||a|a||
008 190606s2017 xx obm 000 0 eng d
020 $a 9780355302035
035 $a (MiAaPQ)AAI10608662
035 $a (MiAaPQ)umd:18330
035 $a AAI10608662
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Fakhraei, Seyed Shobeir. $3 1190634
245 1 0 $a Collective Multi-Relational Network Mining.
264 0 $c 2017
300 $a 1 online resource (149 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.
500 $a Adviser: Lise Getoor.
502 $a Thesis (Ph.D.)--University of Maryland, College Park, 2017.
504 $a Includes bibliographical references
520 $a Our world is becoming increasingly interconnected, and the study of networks and graphs are becoming more important than ever. Domains such as biological and pharmaceutical networks, online social networks, the World Wide Web, recommender systems, and scholarly networks are just a few examples that include explicit or implicit network structures. Most networks are formed between different types of nodes and contain different types of links. Leveraging these multi-relational and heterogeneous structures is an important factor in developing better models for these real-world networks. Another important aspect of developing models for network data to make predictions about entities such as nodes or links, is the connections between such entities. These connections invalidate the i.i.d. assumptions about the data in most traditional machine learning methods. Hence, unlike models for non-network data where predictions about entities are made independently of each other, the inter-connectivity of the entities in networks should cause the inferred information about one entity to change the models belief about other related entities.
520 $a In this dissertation, I present models that can effectively leverage the multi-relational nature of networks and collectively make predictions on links and nodes. In both tasks, I empirically show the importance of considering the multi-relational characteristics and collective predictions. In the first part, I present models to make predictions on nodes by leveraging the graph structure, links generation sequence, and making collective predictions. I apply the node classification methods to detect social spammers in evolving multi-relational social networks and show their effectiveness in identifying spammers without the need of using the textual content. In the second part, I present a generalized augmented multi-relational bi-typed network. I then propose a template for link inference models on these networks and show their application in pharmaceutical discoveries and recommender systems. In the third part, I show that my proposed collective link prediction model is an instance of a general graph-based prediction model that relies on a neighborhood graph for predictions. I then propose a framework that can dynamically adapt the neighborhood graph based on the state of variables from intermediate inference results, as well as structural properties of the relations connecting them to improve the predictive performance of the model.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Artificial intelligence. $3 559380
650 4 $a Statistics. $3 556824
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0800
690 $a 0463
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Maryland, College Park. $b Computer Science. $3 1180862
773 0 $t Dissertation Abstracts International $g 79-03B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10608662 $z click for full text (PQDT)