Collective classification in social networks
Classification is one of the most studied subjects in machine learning. Most classification methods that were developed this last decade either account for structure (interactions, relationships) or attributes (text, numerical, etc). This leads to ignoring significant patterns in a dataset that coul...
Gespeichert in:
| Abstract | Classification is one of the most studied subjects in machine learning. Most classification methods that were developed this last decade either account for structure (interactions, relationships) or attributes (text, numerical, etc). This leads to ignoring significant patterns in a dataset that could only be captured by analyzing the features of an item and its interactions. Collective classification methods use both structure and attributes, often by aggregating data from neighbors of a node and learning a model on the aggregated data. In social networks, the degree distribution of nodes follows a power law where few nodes have many neighbors. High degree nodes have incoming links from low degree nodes of different classes and many nodes have very few edges. Hence, using only local structure may lead to poor predictions. Also, many social networks allow for different types of interactions (retweet, reply, like, etc.) that affect classification differently. This article proposes a collective classification method that makes use of the structure of a network to determine its neighbors. It then presents experiments aimed at detecting jihadi propagandists and malware distributors on social networks. |
|---|---|
| AbstractList | Classification is one of the most studied subjects in machine learning. Most classification methods that were developed this last decade either account for structure (interactions, relationships) or attributes (text, numerical, etc). This leads to ignoring significant patterns in a dataset that could only be captured by analyzing the features of an item and its interactions. Collective classification methods use both structure and attributes, often by aggregating data from neighbors of a node and learning a model on the aggregated data. In social networks, the degree distribution of nodes follows a power law where few nodes have many neighbors. High degree nodes have incoming links from low degree nodes of different classes and many nodes have very few edges. Hence, using only local structure may lead to poor predictions. Also, many social networks allow for different types of interactions (retweet, reply, like, etc.) that affect classification differently. This article proposes a collective classification method that makes use of the structure of a network to determine its neighbors. It then presents experiments aimed at detecting jihadi propagandists and malware distributors on social networks. |
| Author | Birregah, Babiga Jaafor, Omar |
| Author_xml | – sequence: 1 givenname: Omar surname: Jaafor fullname: Jaafor, Omar email: omar.jaafor@utt.fr organization: Charles Delaunay Institute, University of Technology of Troyes, Troyes, France – sequence: 2 givenname: Babiga surname: Birregah fullname: Birregah, Babiga email: babiga.birregah@utt.fr organization: Charles Delaunay Institute, University of Technology of Troyes, Troyes, France |
| BookMark | eNqNkM1LxDAQxeMXuLvu2YOXHj3YmmmSJjlK8QsWvCh4C0maQNxuI01R_O_Nsj14FGZ4ML83w_CW6HSIg0PoEnAFQNktAcC4ZtVeoRZHaJmnmFApCTtGi5pyUkoJ7yd_wTlap_SB8yKI3GSBbtrY985O4csVttcpBR-snkIcijAUKdqg-2Jw03cct-kCnXndJ7eedYXeHu5f26dy8_L43N5tSg2ymUpPjRTSW0Lza0Abw7uaEW4991zXVJBOct9YL0E6IzpNMRgiTeOZANbhjqzQ1eFucM6pzzHs9PijJG5y8UyvD1TbnTIxbpMCrPahqDkUNYeSrdU_rcqMwXnyCyUSXg0 |
| ContentType | Conference Proceeding |
| Copyright | 2017 ACM |
| Copyright_xml | – notice: 2017 ACM |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1145/3110025.3110128 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Xplore POP ALL IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE/IET Electronic Library url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science Sociology & Social History |
| EISBN | 1450349935 9781450349932 |
| EISSN | 2473-991X |
| Editor | Diesner, Jana Ferrari, Elena Xu, Guandong |
| Editor_xml | – sequence: 1 givenname: Jana surname: Diesner fullname: Diesner, Jana – sequence: 2 givenname: Elena surname: Ferrari fullname: Ferrari, Elena – sequence: 3 givenname: Guandong surname: Xu fullname: Xu, Guandong |
| EndPage | 835 |
| ExternalDocumentID | 9069067 |
| Genre | orig-research |
| GroupedDBID | 6IE 6IF 6IL 6IN AAWTH ABLEC ACM ALMA_UNASSIGNED_HOLDINGS APO BEFXN BFFAM BGNUA BKEBE BPEOZ CBEJK GUFHI IEGSK LHSKQ OCL RIB RIC RIE RIL |
| ID | FETCH-LOGICAL-a196t-f4b989fc34101146b7d2537cf7f7a2483d97f6cf919eb8da401b39b6f5815d0d3 |
| IEDL.DBID | RIE |
| ISBN | 1450349935 9781450349932 |
| IngestDate | Tue Nov 04 05:04:44 EST 2025 Thu Jul 10 05:53:10 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | false |
| Keywords | Semi-supervised classification unbalanced data collective classification surveillance multi-layer networks social networks |
| Language | English |
| License | Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org |
| LinkModel | DirectLink |
| MeetingName | ASONAM '17: Advances in Social Networks Analysis and Mining 2017 |
| MergedId | FETCHMERGED-LOGICAL-a196t-f4b989fc34101146b7d2537cf7f7a2483d97f6cf919eb8da401b39b6f5815d0d3 |
| PageCount | 9 |
| ParticipantIDs | ieee_primary_9069067 acm_books_10_1145_3110025_3110128 acm_books_10_1145_3110025_3110128_brief |
| PublicationCentury | 2000 |
| PublicationDate | 20170731 2017-July |
| PublicationDateYYYYMMDD | 2017-07-31 2017-07-01 |
| PublicationDate_xml | – month: 07 year: 2017 text: 20170731 day: 31 |
| PublicationDecade | 2010 |
| PublicationPlace | New York, NY, USA |
| PublicationPlace_xml | – name: New York, NY, USA |
| PublicationSeriesTitle | ACM Conferences |
| PublicationTitle | 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) |
| PublicationTitleAbbrev | ASONAM |
| PublicationYear | 2017 |
| Publisher | ACM |
| Publisher_xml | – name: ACM |
| SSID | ssj0002180213 ssj0001615928 |
| Score | 1.6542788 |
| Snippet | Classification is one of the most studied subjects in machine learning. Most classification methods that were developed this last decade either account for... |
| SourceID | ieee acm |
| SourceType | Publisher |
| StartPage | 827 |
| SubjectTerms | collective classification Human-centered computing Human-centered computing -- Collaborative and social computing Human-centered computing -- Collaborative and social computing -- Collaborative and social computing systems and tools Human-centered computing -- Collaborative and social computing -- Collaborative and social computing systems and tools -- Social networking sites Human-centered computing -- Collaborative and social computing -- Collaborative and social computing theory, concepts and paradigms Human-centered computing -- Collaborative and social computing -- Collaborative and social computing theory, concepts and paradigms -- Social media Human-centered computing -- Collaborative and social computing -- Collaborative and social computing theory, concepts and paradigms -- Social networks Human-centered computing -- Human computer interaction (HCI) Human-centered computing -- Human computer interaction (HCI) -- Interaction paradigms Human-centered computing -- Human computer interaction (HCI) -- Interaction paradigms -- Web-based interaction Information systems Information systems -- Information systems applications Information systems -- Information systems applications -- Collaborative and social computing systems and tools Information systems -- Information systems applications -- Collaborative and social computing systems and tools -- Social networking sites Information systems -- Information systems applications -- Data mining Information systems -- World Wide Web Information systems -- World Wide Web -- Web applications Information systems -- World Wide Web -- Web applications -- Social networks multi-layer networks Networks Networks -- Network types Networks -- Network types -- Overlay and other logical network structures Networks -- Network types -- Overlay and other logical network structures -- Online social networks Semi-supervised classification social networks surveillance unbalanced data |
| Title | Collective classification in social networks |
| URI | https://ieeexplore.ieee.org/document/9069067 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3NS8MwFH9sw4MndZu4-UEE0cu6LWmzJGdxeBo7KOxWkjSBHexkH4L_vXlp3RAE8dQPSimvTX_v6_d7AHfUZNZrhqVBp5OMWp8oK1gIVZwxgiqvTRaHTYjZTC4Wat6AwZ4L45yLzWduiLuxll-s7A5TZSMVZXVFE5pCTCqu1iGfEqBZ1a40HjOUNqNpreZDMz5KUR2N8SFuKYvSqvbtx1CViCnTk_89zSl0D-Q8Mt_Dzhk0XNmGk-_pDKRerG3o77ko5J5ULFxSiYJ8dmAQEwbxX0csOtDYMRRfElmWpEqkk7JqEd904XX69PL4nNSDExIdFtQ28ZlRUnkbEArjnYkRBeMpChB5oVkm00IJP7FeUeWMLHSIsUyqzMRzSXkxLtJzaJWr0l0AcWbssDKqwy2C5yWNZFpyPy60V2nGXQ9ugxVzjAg2eUVy5nlt6by2dA8e_rwmN-ul8z3ooJ3z90ppI69N3P_99CUcMwTZ2Dx7Ba3teueu4ch-bJeb9U38PL4Adhe0Vg |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1bS8MwFD7MKejTdJu4eYsg-rJuTZqszbM4Js6xhwl7C0mawB7sZBfBf2-T1g1BEJ96oZRy2vQ7t-87ALdYUW0lcaVBIwOKtQ24jkkeqhilYsytVNQPm4jH42Q245MKdLZcGGOMbz4zXbfra_npQm9cqqzHvaxuvAf7jFISFmytXUYlB2deOtPumDhxMxyVej6Ysl7k9NEI67otJl5cVb_9GKviUWVQ-9_zHENzR89Dky3wnEDFZHWofc9nQOVyrUN7y0ZBd6jg4aJCFuSzAR2fMvB_O6SdC-16hvxrQvMMFal0lBVN4qsmvA4epw_DoBydEMh8Sa0DSxVPuNU5RrmIp6_ilLDISRDZWBKaRCmPbV9bjrlRSSrzKEtFXPUtSzBLwzQ6hWq2yMwZIKNC42qjMr9F7nslKiEyYTZMpeURZaYFN7kVhYsJVqKgOTNRWlqUlm7B_Z_XCLWcG9uChrOzeC-0NkRp4vbvp6_hcDh9GYnR0_j5HI6Ig1zfSnsB1fVyYy7hQH-s56vllf9UvgALHLed |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=Proceedings+of+the+2017+IEEE%2FACM+International+Conference+on+Advances+in+Social+Networks+Analysis+and+Mining+2017&rft.atitle=Collective+classification+in+social+networks&rft.au=Jaafor%2C+Omar&rft.au=Birregah%2C+Babiga&rft.series=ACM+Conferences&rft.date=2017-07-31&rft.pub=ACM&rft.isbn=1450349935&rft.spage=827&rft.epage=835&rft_id=info:doi/10.1145%2F3110025.3110128 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9781450349932/lc.gif&client=summon&freeimage=true |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9781450349932/mc.gif&client=summon&freeimage=true |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9781450349932/sc.gif&client=summon&freeimage=true |