soc mob

Elizabeth F. Churchill

Palo Alto Research Center
Christine A. Halverson
IBM
Social Networks
and Social Networking
14 SEPTEMBER • OCTOBER 2005 Published by the IEEE Computer Society 1089-7801/05/$20.00 © 2005 IEEE IEEE INTERNET COMPUTING Guest Editors’ Introduction
Social networking is built on the idea
that there is a determinable structure
to how people know each other,
whether directly or indirectly. Notions such
as “six degrees of separation” — that
everyone on Earth is separated from everyone
else by no more than six intermediate
personal relationships — have popularized
the idea that people can be (however
unknowingly) connected through common
associates.
This issue’s theme includes three articles
on research activities that have
drawn on ideas from social networking to
drive innovative designs. The focus stays
close to our own intellectual home — the
design, development, and study of social
technologies at the level of individuals,
groups, and organizations — although we
refer to the broader issue of business,
community, and societal impact in this
short introduction.
Origins
Most contemporary lay discussion of
social networking seems to center on
online interactions via the Internet and
focus on “hooking up” with others to get
a job, get a date, or share stories with
people who have, say, the same breed of
dog. Yet, for decades researchers in the
behavioral sciences have been systematically
studying social networks of all
kinds — “offline” interactions (face to
face, letters, telephone, and so on) as well
as online to determine how social networks
are developed and maintained and
how social-network connections affect
our lives.
John Scott’s introduction suggests that
contemporary social network analysis
(SNA)1 draws on three lines of inquiry:
• Sociometric analysts in the US during
the 1930s, whose work had roots in
Gestalt psychology, aimed to investigate
how feelings of well-being are
related to the structure of people’s
social lives. This movement is most
closely associated with Jacob Moreno,
who devised the sociogram, a visual
diagram of people’s relationship networks
in which individuals are represented
as points and their connections
to others as lines. Other major players
in this research movement were Kurt
Lewin, whose greatest legacy was his promotion
of mathematical models of group relations,
and Fritz Heider, who focused on
people’s perceptions about their relationships
with others.
• Also in the 1930s, Harvard University researchers
began focusing on cliques in social groups to
identify cohesive subgroups (such as work,
church, family, associations, and clubs) within
social systems. This group was influenced
by anthropologist Alfred Radcliffe-Brown,
whose work focused on factory and community
life in the US.
• A group of anthropologists in Manchester, England,
also drew on the work of Radcliffe-Brown
in the 1950s. John Barnes, a member of this
group, is attributed with having coined the specific
term “social networks” in 1954. His work
with Elizabeth Bott drew on the sociometric
approach, but focused on people’s informal
social relationships rather than those associated
with institutions and associations. In addition,
their work focused on conflict and change
in these networks. Clyde Mitchell extended the
traditional sociometric approach with insights
from the mathematics of graph theory to better
deal with observations that were gathered.
Influenced by these investigations, Harvard
researchers led by Harrison White further
explored the mathematical basis of social structure
in the 1960s and ’70s. They drew together
algebraic models of groups using set theory and
multidimensional scaling to establish concepts
such as the strength and distance of connections.
The general approach gained legitimacy and
popularity with the publications of Mark Granovetter’s
analyses of how information from
informal social contacts was used in job seeking
in a US community.2,3 These works laid the foundation
for the methods of study and analysis
used in SNA today.
Definitions
SNA data is essentially relational rather than
attribute-based (that is, concerned with relationships
between things versus the attributes of
individual entities). Thus, the unit of analysis
isn’t the individual, but structures (networks) that
consist of at least two social entities (usually
more) and the links among them. Examples of
the data gathered include kinship relations (for
example, brother of ), social roles (boss of, friend
of, and so on), actions (such as has dinner with,
dance with, or fights with), affective (loves,
hates, and so on), material exchanges (such as
business transactions) and common behaviors
(for example, wears the same jeans or goes to the
same tattoo parlor).
Figure 1 is a sociogram depicting the structure
of relations between entities A through G, the
“nodes” in a simple network. In the figure, the circles
are nodes and the lines between them are links
(also called arcs, edges, or ties). Entity A is connected
to two subgroups and one singleton, G. One
subgroup is made up of entities B, C, and D, and
the other comprises entities E and F. The arrows
depict whether the flows are uni- or bidirectional:
• A is connected to — say, sends email to — B, C,
E, F, and G.
• A receives email from B, C, F, and G.
• E and F send email to each other.
• B,C, and D all send and receive email from each
other.
• A doesn’t receive email from E.
We could characterize A as spanning the boundary
between the two subgroups, thus serving as a
potential connection source between individuals
in each. Figure 2 (next page) shows the connectivity
matrix visualized in Figure 1.
Information passes between nodes as flows — the
movement of diseases among cattle populations,
connections between musicians based on musical
IEEE INTERNET COMPUTING www.computer.org/internet/ SEPTEMBER • OCTOBER 2005 15
Social Networking
Figure 1. Elements of a social network, illustrated
in a simple sociogram. The nodes in this network
are represented as circles, and the links or
connections between them are the arrowed lines.
Between the nodes are one unidirectional and
eight bidirectional links. A is at the centre of two
subgroups of linked nodes consisting of B, C, and
D, and E and F, respectively. A also has a
connection to G. A connects to E, but E doesn’t
connect to A.
E
C
A G
F
B D
styles, letters, money, emails, blog entries, gossip,
love, or virtually anything else.
In analyzing the flows between nodes along
links, we can characterize nodes as powerless,
active, stationary, transient, or permanent. Links
can be strong or weak, private or public, singular
or multiple, unique or redundant, and parallel or
intersecting. Flows between nodes can be copious
or sparse, constant or intermittent, one-way or
bidirectional, and meaningful or meaningless.
Using the simple concepts of nodes, ties, and
flows, analysts can derive relational matrices and
sociograms for anything in which connections
exist. Network analysis also reveals substructures
within networks — for example, cliques within a
larger group. Some common network characterizations
are as follows:
• centralized, decentralized (that is, multicentered),
or distributed (centerless);
• hierarchical or horizontal;
• bounded or boundless;
• finite (with fixed limits on the number of nodes
and ties);
• accessible or inaccessible;
• inclusive or exclusive;
• intensive (that is, few nodes linked by a multiplicity
of dense, strong ties) or expansive
(many nodes enabling reciprocal, multidirectional
flows); or
• noninteractive (enabling only unidirectional
flows).
Changing patterns in networks over time show
how networks form, grow, and wane. By understanding
such patterns in different network types,
we can also derive the potential causes and consequences
of change, and predict network evolution
given different interventions.
Why Do Networked
Computers Matter?
The advent of Internet communications has greatly
increased SNA’s popularity in recent years.
Broadly speaking, the Internet has sparked curiosity
(why and how are people connecting with others?),
opportunity (we can track communication
flows efficiently via computer logs), and commerce
(what services will be compelling enough for people
to pay for them?).
For researchers interested in the dynamics of
human communication, it’s fascinating that people
are increasingly available for online communication,
often with others they would never have
encountered prior to the Internet’s emergence.
Connections are no longer as bound by propinquity;
rather, people can seek out or “bump into”
others from all over the globe. Further, the potential
for network density increases with social software
that emphasizes group communications
(Tribe.net, for example, explicitly focuses on
groups and communities as well as person-toperson
contacts). In this regard, the drive for
human-to-human communication has become
more evident — at least initiating, if not maintaining
it, as evidence shows that many “connections”
through online sites are ephemeral.
Along with this surge in sociability among
friends and strangers, computer-based networks
have let researchers instrument and measure what
communications are taking place in evolving, stable,
and fluctuating social networks. As people
increasingly interact online, analysts are interested
in observing and characterizing when, where,
and how connections are made, how long they are
maintained and in what ways, and what function
these connections serve. At the level of interpersonal
communication and communities of interest
and affiliation, researchers have examined
social information flows for strong and weak ties
in Internet communications4 and online spaces,5–7
how online socializing impacts people’s psychological
health,8 and how online socializing affects
face-to-face interactions in communities.9 Considerable
research is also exploring impression
management — that is, how people represent
themselves through constructed online identities.10
At the societal level, Manuel Castells places computer-
based communications at the center of
16 SEPTEMBER • OCTOBER 2005 www.computer.org/internet/ IEEE INTERNET COMPUTING
Guest Editors’ Introduction
Figure 2. Connectivity matrix for entities A through
G in Figure 1. In the matrix, a 1 indicates a
connection, and a 0 shows no connection. The
absence of a connection between A and D is shown
by the 0 in both cells. A is also connected to E via a
unidirectional connection: E does not connect to A.
G 1 0 0 0 0 0 1
F 1 0 0 0 1 1 0
E 0 0 0 0 1 1 0
D 0 1 1 1 0 0 0
C 1 1 1 1 0 0 0
B 1 1 1 1 0 0 0
A 1 1 1 0 1 1 1
G F E D C B A
changes in global socioeconomics.11,12 He isn’t
alone in expressing concerns about rising
inequities between those with access to technical
— and therefore social — networks, and those with
such access.
Social-network concepts have become
increasingly interesting to companies such as
Ryze, LinkedIn, MySpace, Tribe, Orkut, and
Friendster, which have launched networking sites
in the past few years, although no particularly
lucrative business model has emerged. At the
inter- and intraorganizational levels, analysts
have used SNA to map the ways in which people
communicate and cooperate — that is, to identify
knowledge flows: Who do people seek information
and knowledge from? Who do they share
their information and knowledge with? As
applied to business, SNA is often about revealing
the informal communication networks that exist
within organizations — how information actually
flows around and between the formal procedures
and relationships mapped to organizational
hierarchy charts. Several consultancy firms are
offering services based on SNA, promising optimization
of information flow as a way to improve
efficiency, reduce costs, and improve productivity.
Within the research context, understanding
how these informal networks flow within and
among organizations has even given rise to a
separate area of study, called organizational network
analysis (ONA).13,14
Articles in this Issue
All three theme articles take up social networks
and social networking in terms of relationships
among individuals (rather than at the organizational,
community, or societal level). In addition,
all share our own penchant for sociotechnical
design intervention — that is, all are concerned
with using SNA to drive innovations that help
people use communication technologies to
understand and manage their social networks
more effectively.
Danyel Fisher’s “Using Egocentric Networks to
IEEE INTERNET COMPUTING www.computer.org/internet/ SEPTEMBER • OCTOBER 2005 17
Social Networking
Resources on Social Networks, Social Networking, and Social-Network Analysis
Simply typing “social network” into a
search engine will yield thousands of
hits pointing to papers, books, journals, and
bibliographies, as well as tools for analyzing
and visualizing social networks. Here, we
offer a more focused list of relevant readings
and tools.
Readings and Resources
Wikipedia offers a good basic introduction
to social networks and social-network
analysis (SNA), with links to numerous
resources (http://en.wikipedia.org/wiki/
Social_networks).
Several academic bibliographies dedicated
to social networks and SNA are available
online. (See www.socialnetworks.org,
for a good example.)
NetLab (www.chass.utoronto.ca/~well
man/netlab/) provides up-to-date information
on social networks in the broadest
sense, including pointers to many activities
and resources that intersect with SNA. This
is an excellent, scholarly resource.
Robert Hanneman from the University
of California and Mark Riddle from the
University of Colorado maintain a particularly
good bibliography of SNA resources
at http://faculty.ucr.edu/~han
neman/nettext/Bibliography.html.
Bruce Hopper and Patti Anklam also
maintain a fairly good, annotated SNA bibliography
at http://connectedness.blog
spot.com/2005/05/annotated-biblio
graphy-of-social.html.
Specifically business-related resources
are often listed under the banner of organizational
network analysis (ONA), which
has been dubbed an x-ray into the inner
workings of an organization.Rob Cross and
colleagues have focused on ONA in their
work (www.robcross.org/sna.htm).
Network-analysis modeling techniques
can be quite complicated as researchers
use considerable mathematical rigor and
sophisticated statistical techniques to
uncover patterns of nodes, links, and flows.
We recommend beginning with John
Scott’s Social Network Analysis:A Handbook
(Sage Publications, 1991). Scott offers an
excellent introduction to the area.
A good follow-up is Stanley Wasserman
and Joseph Galaskiewizc’s Advances in Social
Network Analysis (Sage publications, 1994),
as well as Wasserman and Katherine Faust’s
Social Network Analysis (Cambridge Univ.
Press, 1994).
Darin Barney’s The Network Society
(Polity Press, 2004) offers an interesting
introduction to the broader area of the
Networked Society, inviting readers to
consider the larger scale (global, for
example) ramifications of sociotechnical
networks.
Elsevier publishes an excellent journal
called Social Networks (www.elsevier.com/
wps/find/journaldescription.cws_home/505
596/description). See also the online Journal
of Social Structure (www.cmu.edu/joss/).
Tools
Social networking is an increasingly hot topic
in software design. Sites and services are
built around common interests, geographical
proximity,professional communities and
practices, and so on. Social networking sites
such as Ryze, LinkedIn, Friendster, Orkut,
MySpace, and Tribe are growing in popularity,
although effective revenue generation
remains elusive.
continued on p. 18
18 SEPTEMBER • OCTOBER 2005 www.computer.org/internet/ IEEE INTERNET COMPUTING
Guest Editors’ Introduction
Understand Communication” stays closest to the
current characterizations of social networking
through online communication. Focusing on the
networked “ego” of the sophisticated email user,
he presents two systems: Soylent looks at interaction
patterns in email, and Roles applies SNA to
messages and replies within Usenet. Both projects
examine connections that are explicit and volitional
— connections are based on conscious decisions
to communicate, as opposed to, say, bumping
into someone serendipitously in a hallway — with
no assumption that links are bidirectional. Soylent
presents sociograms as end-user visualizations of
connections between individuals who’ve been
coaddressed on email messages. Fisher describes
core patterns that emerge from such connections.
The Roles project applies SNA to public Usenet
group communications to identify individuals’
roles as well as point toward interaction patterns
between individuals.
In “Social Networks as Health Feedback Displays,”
Margaret Morris also focuses on individuals,
concentrating on self-perception and mental
well-being. Her work at Intel takes a proactive
approach to health by using social-networking
and pervasive computing technologies to help
reduce feelings of social isolation and depression
in elderly individuals. Building on cognitive
behavioral ideas and notions of mindfulness,
Morris and colleagues use network displays to
provide a form of social biofeedback. They use
sensor data (measuring phone calls and visits) to
derive public displays of social interactions with
relations and friends, which they introduced into
select elders’ homes. This approach shows the
persuasive power of mobilizing concepts such as
social networks: as people see their social interactions
illustrated in these feedback displays,
their feelings of social isolation are subtly and
gently refuted.
Finally, Quentin Jones and Sukeshini A.
Grandhi’s “P3 Systems: Putting the Place Back
into Social Networks” takes us furthest from current
discussions of social networking. Although
very much part of early SNA work, geographical
space, location, and architectural space are often
forgotten in discussions of abstract “connections”
via communication technologies. This article
brings together physical place, mobile
technologies, and social networks in what the
authors call the P3 framework, which is intended
to help designers consider what geographic
context cues are appropriate for specific social
interactions. In their framework, Jones and
Grandhi distinguish between people- and placecentered
techniques for communication or location-
aware community systems. As we see an
increase in cellular technologies that promise
perpetual availability, it seems there will also be
an increase in tools and applications for social
Resources on Social Networks, Social Networking, and Social-Network Analysis cont.
Mobile social software (MoSoSo) services
and applications are increasingly popular.
Similarly, building and maintaining social
networks by sharing digital media is becoming
more common, both online (Flickr’s
photo-sharing site, for example;www.flickr.
com) and offline (Fuji Xerox’s interactive
bulletin boards, the CollaboPosters; see
www.designingassociates.com/displays).Undoubtedly,
the future will bring new visions
for such sites and services.
Various tools have also emerged for
visualizing explicit and tacit social networks
and carrying out SNA. We can apply such
tools and metrics at the level of individuals,
organizations, and industries to analyze
computer networks (to optimize topologies,
and so on) and information systems
(to offer representations of link structures,
for example). These tools reveal densely or
sparsely connected clusters, which can be
mapped to “affiliative groups” or communities
of practice to reveal people who are
connectors and boundary spanners
between groups.
Tools are designed for different areas
and levels of investigation – for example,
some are better suited for social-science
research and others for business analysis.
They also differ in the level of mathematical
understanding they assume, and in
their ability to deal with large data sets.
As with all forms of data analysis, selecting
the “right” tool depends on the questions
posed, desired output, specifics of
the data sets to be analyzed, and the analysts’
interest in manipulating the underlying
parameters.
Orgnet.com’s InFlow 3.0 is frequently
used in business contexts. The site also
includes a good range of articles on SNA as
well as product information on InFLow,
which the company describes as “a social
network mapping and measurement tool.”
Other examples of SNA software
include NetMiner (www.netminer.com/
NetMiner/home_01.jsp), SociometryPro
(www.sociometry.ru/eng/index.php), Pajek
(see http://vlado.fmf.uni-lj.si/pub/neworks/
pajek/), and UCINET (see www.analytic
tech.com/ucinet_5_description.htm).
We also recommend checking the
International Network for Social Network
Analysis (www.insna.org/INSNA/soft_inf.
html) for more pointers to SNA tools and
techniques.
continued from p. 17
networking via these devices. Examples of such
mobile social software (MoSoSo) services include
Dodgeball (www.dodgeball.com), which connects
people to their friends on the basis of physical
proximity, and Morca (www.common.net), which
helps people discover common interests from
each other’s profiles, indexed by their email
addresses. Jones and Grandhi’s framework begins
to address the complexities inherent in making
judgments about our availability by bringing
into focus the fact that desire for contact is moderated
by who is contacting us and where we are
at the time.
Central to SNA is the interplay between the
activities of nodes and the dynamics of the
networks they’re part of. The Internet has made
us aware of people’s desires and abilities to network
socially beyond the confines of geographical
proximity.
The articles in this special issue attempt to
honor the actions of the nodes (the individuals)
while keeping in mind the bigger picture of collective
behavioral patterns. Although the tools
described here are all intended for individuals,
each article highlights how new technologies and
technical competencies will further push our
understanding of human social-networking drives
and desires. Specifically, socially adaptive
location-aware technologies, large-screen displays,
and visualization methods for quickly representing
group dynamics and socio- (rather than
bio-) feedback will surely highlight even more
about how people establish, manage, and maintain
their social networks in mediated and faceto-
face communication situations — and, for that
matter, manage their identities and relationships
as there are more and more ways to connect and
“be connected to.”
Although we can’t do full justice to the theme
topic in terms of sociological analysis, communication-
tool development, personal experience, or
business analysis and applications, we hope this
special issue proves provocative.
References
1. J. Scott, Social Network Analysis: A Handbook, 2nd ed.,
Sage Publications, 1991.
2. M. Granovetter, “The Strength of Weak Ties,” Am. J. Sociology,
vol. 78, no. 6, May 1973, pp. 1360–1380.
3. M. Granovetter, Getting a Job. A Study of Contacts and
Careers, Harvard Univ. Press, 1974.
4. B. Wellman and M. Gulia, “Virtual Communities as Communities:
Net Surfers Don’t Ride Alone,” Networks in the
Global Village: Life in Contemporary Communities, B. Wellman,
ed., Westview, 1999, pp. 331–366.
5. H. Rheingold, The Virtual Community: Homesteading on
the Electronic Frontier, Addison-Wesley, 1993.
6. L. Cherny, Conversation and Community: Discourse in a
Virtual World, CSLI Publications, 1999.
7. E.F. Churchill and S. Bly, “Virtual Environments at Work:
Ongoing Use of MUDs in the Workplace,” Proc. Int’l Joint
Conf. Work Activities Coordination and Collaboration, ACM
Press, 1999, pp. 99–108.
8. R.E. Kraut, B. Butler, and J. Cummings, “The Quality of
Social Ties Online,” Comm. ACM, vol. 45, no. 7, 2002, pp.
103–108.
9. K. Hampton and B. Wellman, “Neighboring in Netville:
How the Internet Supports Community and Social Capital
in a Wired Suburb,” City and Community, vol. 2, no. 4,
2003, pp. 277–311.
10. J. Sunden, Material Virtualities: Approaching Online Textual
Embodiment, Peter Lang Publishing, 2003.
11. M. Castells, The Rise of the Network Society — The Information
Age: Economy, Society and Culture, vol. 1, Blackwell,
1996.
12. M. Castells, The Internet Galaxy: Reflections on the Internet,
Business, and Society, Oxford Univ. Press, 2001.
13. R. Cross, N. Nohria, and A. Parker, “Six Myths about Informal
Networks and How To Overcome Them,” Sloan Management
Rev., vol. 43, no. 3, 2002, pp. 67–75.
14. R. Cross, A. Parker, and S. Borgatti, “Making Invisible Work
Visible: Using Social Network Analysis To Support Strategic
Collaboration,” Calif. Management Rev., vol. 44, no. 2,
2002, pp. 25–46.
Elizabeth F. Churchill is a research scientist at Palo Alto
Research Center (PARC). Originally a psychologist by training,
her research interests center on designing and evaluating
technologies, tools, and smart environments to
facilitate content sharing and communication. Churchill
has a PhD in cognitive science from the University of Cambridge.
She has authored numerous publications and
coedited several books concerned with people’s interactions
in physical and digital spaces. Contact her at churchill@
acm.org.
Christine A. Halverson is a research staff member at IBM. Her
interests include aspects of social interaction, whether in
complex work places, online communities, or daily life.
Halverson has a PhD in cognitive science from the University
of California, San Diego. She is coeditor of
Resources, Co-Evolution, and Artifacts: Theory in CSCW
(to be published by Springer in 2006). Contact her at
krys@acm.org.
IEEE INTERNET COMPUTING www.computer.org/internet/ SEPTEMBER • OCTOBER 2005 19
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