Computational analysis of large and time-dependent social networks

Lauri Kovanen

Computational analysis of large and time-dependent social networks

Julkaisun otsikon käännös: Suurten ja aikariippuvien sosiaalisten verkostojen laskennallinen analyysi

Lauri Kovanen

Tutkimustuotos: Doctoral Thesis › Collection of Articles

Abstrakti

Kompleksiset järjestelmät koostuvat suuresta määrästä alkioita, joiden keskinäiset vuorovaikutukset ovat monimutkaisia; esimerkiksi ihmisaivoja, yhteiskuntaa, internettiä ja biologisia organismeja voidaan kaikkia mallintaa kompleksisina järjestelminä. Matemaattisesti näitä järjestelmiä on luontevaa käsitellä verkostoina, jotka koostuvat solmuista ja niitä yhdistävistä kaarista. Suuria empiiriseen dataan perustuvia verkostoja tutkiva tieteenala tunnetaankin nykyään nimellä "kompleksiset verkostot". Ensimmäiset alaan liittyvät tutkimukset julkaistiin 90-luvun lopulla, ja sen jälkeen lähestymistapaa on käytetty erilaisten teknologisten, biologisten sekä sosiaalisten verkostojen analysointiin. Myös kompleksisia verkostoja käsitteleviä oppikirjoja on julkaistu. Sosiaalisten verkostojen historia on kuitenkin tätä pidempi, sillä niitä on tutkittu jo vuosikymmeniä sosiologiassa, sosiaalipsykologiassa sekä antropologiassa. Sosiaalisen verkoston vaikutus yksilön toimintakykyyn ja hyvinvointiin on nykyään hyvin tunnettu. Elektroninen tiedonvälitys—matkapuhelut, sähköposti, internetin sosiaalisen median palvelut ja jopa monen pelaajan tietokonepelit—on kuitenkin muuttanut merkittävästi tutkittavissa olevien sosiaalisten verkostojen kokoa ja laatua. Suurin tähän asti analysoitu sosiaalinen verkosto sisältää tietoja yli 700 miljoonasta ihmisestä, ja tässä väitöskirjassa analysoitu matkapuhelindata käsittää yli 6 miljoonan ihmistä. Tehokkaat tietokoneet yhdessä valtavien tietokantojen kanssa ovat luomassa uutta, laskennallista sosiaalitiedettä. Tässä väitöskirjassa tutkitaan useita suurten sosiaalisten verkostojen ominaisuuksia. Matemaattisia malleja käytetään usein apuna kun halutaan selittää sosiaalisten verkostojen rakennetta. Väitöskirjan ensimmäinen artikkeli tutkii erilaisten kirjallisuudessa esitettyjen mallien ominaisuuksia. Toinen paljon tutkittu aihe on verkostojen modulaarinen rakenne. On yleisesti tunnettua, että lähes kaikki empiiriset verkostot ovat modulaarisia: ne sisältävät paikallisia kaaritihentymiä. Moduulien tunnistaminen on kuitenkin osoittautunut huomattavan vaikeaksi ongelmaksi sekä teoreettisesti että käytännössä. Tunnistus-menetelmien toimintaa voidaan tutkia arvioimalla niiden empiirisestä verkostosta löytämiä moduuleja. Aika on keskeinen elementti kaikessa vuorovaikutuksessa, mutta jää huomioimatta jos sosiaalista vuorovaikutusta tutkitaan vain verkostojen avulla. Temporaalisten verkostojen avulla voidaan käsitellä verkostomaista dataa, jossa vuorovaikutukset riippuvat ajasta, kuten matkapuheluiden muodostamaa rakennetta. Väitöskirjan viimeinen osa esittelee menetelmän, jonka avulla voidaan analysoida toistuvia rakenteita temporaalisissa verkostoissa.

Julkaisun otsikon käännös	Suurten ja aikariippuvien sosiaalisten verkostojen laskennallinen analyysi
Alkuperäiskieli	Englanti
Pätevyys	Tohtorintutkinto
Myöntävä instituutio	Aalto-yliopisto
Ohjaaja	Saramäki, Jari, Vastuuprofessori Saramäki, Jari, Ohjaaja
Kustantaja	Aalto University
Painoksen ISBN	978-952-60-5165-9
Sähköinen ISBN	978-952-60-5164-2
Tila	Julkaistu - 2013
OKM-julkaisutyyppi	G5 Artikkeliväitöskirja

Tutkimusalat

kompleksiset systeemit
verkostoteoria
sosiaaliset verkostot
aikariippuvat verkostot

Pääsy asiakirjaan

http://urn.fi/URN:ISBN:978-952-60-5164-2

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@phdthesis{bebb31f0b81345a1a2e848de948b2d77,

title = "Computational analysis of large and time-dependent social networks",

abstract = "Complex systems consist of a large number of elements that interact in a non-trivial way; for example the human brain, society, Internet, and biological organisms can all be modelled as complex systems. Complex systems can be naturally represented as networks, mathematical objects that consist of nodes and edges connecting these nodes, and the study of large networks based on empirical data has become known as complex networks. Since the first articles on complex networks appeared in the end of the 1990's, various technological, biological, and social networks have been analyzed. In recent years introductory text books on the subject have also been published. The study of social networks of course has a longer history. Small social networks have been studied for decades in sociology, social psychology and anthropology, and the influence that social networks have on both performance and well being of individuals has been well documented. The availability of electronic communication records—mobile phone calls, emails, online social networking sites and even multiplayer computer games—have changed the scale and detail at which social networks can be analyzed. The largest data set studied so far includes over 700 million individuals, and the mobile phone call records studied in this Thesis contain information of over 6 million people. The combination of powerful computers and large data sets have enabled the emergence of computational social science. Several aspects of large social networks are studied in this Thesis. Models of social networks are commonly used as a way to gain insight about the structure of these networks. The first article studies a number of models suggested for social networks and discusses their advantages and shortcomings. The community structure of various networks has also been a subject of great interest. It is widely accepted that nearly all networks have modular structure, evidenced by local densifications of connectivity. However, identifying communities in empirical data has turned out to be difficult both theoretically and in practice. We apply three state-of-art community detections methods to a large social network and evaluate the quality of the identified communities. One important aspect of human interactions is omitted when analyzing networks: time. Temporal networks have become a common framework for studying data sets where the relations between nodes vary with time, and this framework can be readily applied to study mobile phone calls. The last part of this Thesis introduces the concept of temporal motifs—recurring patterns of events in temporal networks—that can be used to analyze the meso-scale structure of temporal networks.",

keywords = "complex systems, complex networks, social networks, temporal networks, kompleksiset systeemit, verkostoteoria, sosiaaliset verkostot, aikariippuvat verkostot, complex systems, complex networks, social networks, temporal networks",

author = "Lauri Kovanen",

year = "2013",

language = "English",

isbn = "978-952-60-5165-9",

series = "Aalto University publication series DOCTORAL DISSERTATIONS",

publisher = "Aalto University",

number = "81",

address = "Finland",

school = "Aalto University",

}

TY - BOOK

T1 - Computational analysis of large and time-dependent social networks

AU - Kovanen, Lauri

PY - 2013

Y1 - 2013

N2 - Complex systems consist of a large number of elements that interact in a non-trivial way; for example the human brain, society, Internet, and biological organisms can all be modelled as complex systems. Complex systems can be naturally represented as networks, mathematical objects that consist of nodes and edges connecting these nodes, and the study of large networks based on empirical data has become known as complex networks. Since the first articles on complex networks appeared in the end of the 1990's, various technological, biological, and social networks have been analyzed. In recent years introductory text books on the subject have also been published. The study of social networks of course has a longer history. Small social networks have been studied for decades in sociology, social psychology and anthropology, and the influence that social networks have on both performance and well being of individuals has been well documented. The availability of electronic communication records—mobile phone calls, emails, online social networking sites and even multiplayer computer games—have changed the scale and detail at which social networks can be analyzed. The largest data set studied so far includes over 700 million individuals, and the mobile phone call records studied in this Thesis contain information of over 6 million people. The combination of powerful computers and large data sets have enabled the emergence of computational social science. Several aspects of large social networks are studied in this Thesis. Models of social networks are commonly used as a way to gain insight about the structure of these networks. The first article studies a number of models suggested for social networks and discusses their advantages and shortcomings. The community structure of various networks has also been a subject of great interest. It is widely accepted that nearly all networks have modular structure, evidenced by local densifications of connectivity. However, identifying communities in empirical data has turned out to be difficult both theoretically and in practice. We apply three state-of-art community detections methods to a large social network and evaluate the quality of the identified communities. One important aspect of human interactions is omitted when analyzing networks: time. Temporal networks have become a common framework for studying data sets where the relations between nodes vary with time, and this framework can be readily applied to study mobile phone calls. The last part of this Thesis introduces the concept of temporal motifs—recurring patterns of events in temporal networks—that can be used to analyze the meso-scale structure of temporal networks.

AB - Complex systems consist of a large number of elements that interact in a non-trivial way; for example the human brain, society, Internet, and biological organisms can all be modelled as complex systems. Complex systems can be naturally represented as networks, mathematical objects that consist of nodes and edges connecting these nodes, and the study of large networks based on empirical data has become known as complex networks. Since the first articles on complex networks appeared in the end of the 1990's, various technological, biological, and social networks have been analyzed. In recent years introductory text books on the subject have also been published. The study of social networks of course has a longer history. Small social networks have been studied for decades in sociology, social psychology and anthropology, and the influence that social networks have on both performance and well being of individuals has been well documented. The availability of electronic communication records—mobile phone calls, emails, online social networking sites and even multiplayer computer games—have changed the scale and detail at which social networks can be analyzed. The largest data set studied so far includes over 700 million individuals, and the mobile phone call records studied in this Thesis contain information of over 6 million people. The combination of powerful computers and large data sets have enabled the emergence of computational social science. Several aspects of large social networks are studied in this Thesis. Models of social networks are commonly used as a way to gain insight about the structure of these networks. The first article studies a number of models suggested for social networks and discusses their advantages and shortcomings. The community structure of various networks has also been a subject of great interest. It is widely accepted that nearly all networks have modular structure, evidenced by local densifications of connectivity. However, identifying communities in empirical data has turned out to be difficult both theoretically and in practice. We apply three state-of-art community detections methods to a large social network and evaluate the quality of the identified communities. One important aspect of human interactions is omitted when analyzing networks: time. Temporal networks have become a common framework for studying data sets where the relations between nodes vary with time, and this framework can be readily applied to study mobile phone calls. The last part of this Thesis introduces the concept of temporal motifs—recurring patterns of events in temporal networks—that can be used to analyze the meso-scale structure of temporal networks.

KW - complex systems

KW - complex networks

KW - social networks

KW - temporal networks

KW - kompleksiset systeemit

KW - verkostoteoria

KW - sosiaaliset verkostot

KW - aikariippuvat verkostot

KW - complex systems

KW - complex networks

KW - social networks

KW - temporal networks

M3 - Doctoral Thesis

SN - 978-952-60-5165-9

T3 - Aalto University publication series DOCTORAL DISSERTATIONS

PB - Aalto University

ER -

Computational analysis of large and time-dependent social networks

Abstrakti

Tutkimusalat

Pääsy asiakirjaan

Sormenjälki

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