Networks
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This document discusses theories of complex networks. It introduces scale-free networks and preferential attachment, where new nodes are more likely to connect to existing popular nodes. This leads to a few hubs with many connections and many nodes with few connections, rather than a random distribution. Implications include that popularity begets more popularity, niches are important, older nodes tend to be more connected, and money alone does not guarantee future popularity but connecting to existing popular nodes can help growth. Networks cluster with dense connections within communities and sparser connections between communities.
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Similar to Networks
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Third part (59-68)
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This document discusses how communities can be built and strengthened through network weaving. It describes how a regional economic development organization in Appalachian Ohio called ACEnet mapped the social and economic connections in the local food industry to better understand the network. The document outlines that improved connectivity starts with mapping the existing network to understand it, and then iteratively improving connectivity by introducing disconnected groups and facilitating collaboration between nodes. It describes the typical phases a community network goes through, from scattered fragments to a multi-hub small world network. It emphasizes the important role of network weavers in intentionally facilitating new connections to create a more vibrant and resilient community network.
COMMUNICATIONS OF THE ACM November 2004Vol. 47, No. 11 15.docx
COMMUNICATIONS OF THE ACM November 2004/Vol. 47, No. 11 15
N
etworks are hot. The
Internet has made it pos-
sible to observe and mea-
sure linkages
representing relationships of
all kinds. We now recognize
networks everywhere: air
traffic, banking, chemical
bonds, data communications,
ecosystems, finite element
grids, fractals, interstate
highways, journal citations,
material structures, nervous
systems, oil pipelines, orga-
nizational networks, power
grids, social structures, trans-
portation, voice communica-
tion, water supply, Web
URLs, and more.
Several fields are collabo-
rating on the development of
network theory, measurement,
and mapping: mathematics
(graph theory), sociology (net-
works of influence and communi-
cation), computing (Internet), and
business (organizational net-
works). This convergence has pro-
duced useful results for risk
assessment and reduction in com-
plex infrastructure networks,
attacking and defending networks,
protecting against network con-
nectivity failures, operating busi-
nesses, spreading epidemics
(pathogens as well as computer
viruses), and spreading innova-
tion. Here, I will survey the fun-
damental laws of networks that
enable these results.
Defining a Network
A network is usually defined as a
set of nodes and links. The nodes
represent entities such as persons,
machines, molecules, documents,
or businesses; the links represent
relationships between pairs of
entities. A link can be directed
(one-way relationship) or undi-
rected (mutual relationship). A
hop is a transition from one node
to another across a single link
separating them. A path is a series
of hops. Networks are very gen-
eral: they can represent any kind
of relation among entities.
Some common network
topologies (interconnection pat-
terns) have their own names:
clique or island (a connected sub-
network that may be isolated
from other cliques), hierarchical
network (tree structured), hub-
and-spoke network (a special
node, the hub, connected directly
to every other node), and multi-
hub network (several hubs con-
nected directly to many nodes).
Some network topologies are
planned, such as the electric grid,
the interstate highway system, or
Network Laws
M
IC
H
A
EL
S
LO
A
N
Peter J. Denning
Many networks, physical and social, are complex and scale-invariant.
This has important implications from the spread of epidemics and
innovations to protection from attack.
The Profession of IT
16 November 2004/Vol. 47, No. 11 COMMUNICATIONS OF THE ACM
the air traffic system; others are
unplanned. In his seminal papers
about the Internet, Paul Baran
proposed that a planned, distrib-
uted network would be more
resilient to failures than a hub-
and-spoke network.
A host of physical systems eas-
ily fit a network model. Perhaps
less obvious is that human social
networks also fit the model. The
individuals of an organization are
linked by their relationships—
who emails whom, who seeks
advice from whom, or who influ-
ences w.
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On A Quest for Combating Filter Bubbles and Misinformation.
Invited Talk, Chinese University of Hong Kong at Shenzhen, Dec 13, 2022.
Social media have greatly facilitated access to information and news and have enhanced users' ability to share with peers their views on issues. However, they have unfortunately led to increased societal polarization. At the center of this phenomenon are filter bubbles and misinformation. Filter bubbles are the result of excessive personalization which enhances relevance of content at the price of limiting exposure to a specific viewpoint. These bubbles are amplified by the so-called echo chambers that exist in social media, whereby members of a community mutually reinforce a fixed opinion or viewpoint on an issue. Misinformation as well as disinformation, on the other hand, tends to propagate through the network, often faster and more virally than truth.
Both problems manifest themselves in the form of groups of actors working in concert and providing mutual reinforcement. How can we recognize these groups? Having detected them, how can we counteract these problems? The first question can benefit from an examination of techniques developed to search for dense subgraphs in an underlying network. As for the second question, a natural approach for countering filter bubbles is to launch some kind of counter-campaign to balance users' exposure to viewpoints. Countermeasures for misinformation propagating through a network depend on the party planning the countermeasure. The network host can intervene and take steps to limit the propagation of misinformation, but these actions come with a cost. Besides the political sensitivity and cost of limiting freedom of expression, what if the intervention was by mistake done on genuine information? On the other hand, a third party interested in countering the propagation of misinformation may launch a counter-campaign. Some of the ideas behind designing such campaigns have strong connections to a classic problem called Influence Maximization, studied in a very different context, driven by different applications like viral marketing, infection containment, and revenue or welfare maximization. In this talk, we will examine research on detecting dense subgraphs as well as competitive influence maximization and discuss how that can inspire techniques for addressing the two problems above.
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This research paper is published in a research journal titled ‘Pragyaan’, a bi-annual publication of Indian Management Studies, Dehradun.
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- 2. @
- 3. Reading Barabasi, A-L, and Bonabeau, E (2003), Scale-Free Networks. Scientific American, May 2003 Benkler (2006), chapter 7 Terranova (2004), chapter 2 http://www.barabasilab.com/index.php
- 4. Learning outcomes To understand the non-random characteristics of complex networks To apply theoretical models to the www and to social networks To consider implications for public relations
- 6. Random and scale-free networks
- 7. Normal and power-law distribution Benkler (2006): 244
- 8. @
- 9. Barabasi and Albert (1999) The probability that any node on the network will be very highly connected to many others is VERY LOW The probability that a very large number of nodes will be connected very loosely or not at all is VERY HIGH Preferential attachment: new nodes prefer to attach to well-attached nodes
- 10. Huberman and Adamic (1999) Each website has an intrinsically different growth rate New sites are formed at an exponential rate PREFERENTIAL ATTACHMENT + GROWTH = ?
- 12. black: opinion leaders red : influenced green : uninfluenced grey : undecided Viral marketing http://www.orgnet.com Hubs: ‘ broadcast’ weakly infectious viruses, ideas
- 13. Implications The more popular you are, the more popular you become Niches are important Older nodes (sites) tend to be more popular than new ones, but only on average Money alone is not enough to guarantee future popularity or growth, but relevance and connection to already popular nodes can be
- 14. Clustering Sites cluster into densely-linked regions or communities of interest They link much more to each other than to nodes outside Clustering increases and intensifies as you move along the “long tail”
- 15. 28%: heavily interlinked: multiple redundant paths 22%: link to core, but not from core; new, or lower-interest sites 22%: link from core, but not to core; doc depositories or internal org sites 22%: cannot reach or be reached from core 10%: entirely isolated Benkler (2006): 248-9
- 16. Benkler (2006): 252 Distribution becomes more normal in smaller clusters
- 17. Summary “ Bow tie” model repeats itself within clusters As clusters become smaller, attention is more evenly spread Very very few are receiving no attention at all
- 18. The small worlds effect