Masked vigilantes seem to be quite the socialites, when it comes to the company thy keep – or are they? Find out for yourself by taking a closer look at the graphic version of the Marvel Universe’s own social network. All characters with at least 100 mutual inked appearances are present, coloured according to their team or universe affiliations. For a more detailed analysis, follow the link, for all the fans who already know all there is to know: enjoy the visual feast!
Wednesday, 27 February 2013
Tuesday, 19 February 2013
Inaugural Networks
Presidential inaugural speeches in the US
provide a good indication of the forthcoming political agenda. There has been a
lot of research dedicated to this subject, however most of it focuses on
keyword frequency analysis, which makes it difficult to trace the change in
political agenda over the years. The reason is that the public political
discourse is quite predictably dominated with such notions as “people”,
“nation”, “world”. What’s interesting, however, is to detect the moments when
the new notions are introduced into the political agenda, as well as to trace
the change in relationships between the terms. This is where text network
analysis can be quite useful, so Nodus Labs created a special report for The
Guardian newspaper based on the US presidents inauguration speeches from
Nixon’s 1969 to Obama’s 2013 address.
The analysis used the method for text network analysis. The basic premise of this approach is that every word is represented as a node and their co-occurrence within the same context is represented as an edge in the network. After a series of transformations (performed by Textexture software developed by Nodus Labs) the graph is produced, which is then aligned according to Force Atlas algorithm. The nodes (words) that are connected (co-occur within the same context) are pulled together, while the nodes that are not connected are pushed. The resulting aligned graph gives a very good representation of the major semantic fields present within the text. Furthermore, community detection algorithms are applied to the resulting network, sorting the nodes (words) into the different groups according to how interconnected they are to one another. Every community is represented with a different color. As a result, if two words co-occur often together inside the same text they will be positioned next to each other on the graph and also belong to the same community (and, thus, have the same color on the graph). These communities represent the topics inside the text. Finally, the nodes are ranked according to their betweenness centrality measure: the bigger the node, the more different communities it belongs to.
It’s worth noting that such approach is very different from so-called “tag clouds”. Tag clouds show the most frequently mentioned words and they rarely position these words according to their proximity within the text. Therefore, one can get a general idea of the vocabulary inside the text, but it’s very hard to have a sense of the meaning that is produced using this vocabulary. Text network visualization, on the other hand, emphasizes both the most frequently mentioned words, as well as the relationships between them, making it much easier to understand what the text is about. Furthermore, it can also detect the topics inside the text, making it a much more useful tool for improving text comprehension and providing a much more useable interface for text navigation.
The master of rhetorics, Obama combines the best of his predecessors in this inauguration speech. No wonder the “word” has such high relevance in his speech – it refers to the moments Obama is quoting someone else. In 2013’s speech the “time” and “require” probably relates to the fact that Obama had to respond to all the criticism that something had to be done immediately about the state of US economy and politics – and he successfully addressed these concerns.
Source:
See more, about the text network analysis:
http://noduslabs.com/research/pathways-meaning-circulation-text-network-analysis/
The analysis used the method for text network analysis. The basic premise of this approach is that every word is represented as a node and their co-occurrence within the same context is represented as an edge in the network. After a series of transformations (performed by Textexture software developed by Nodus Labs) the graph is produced, which is then aligned according to Force Atlas algorithm. The nodes (words) that are connected (co-occur within the same context) are pulled together, while the nodes that are not connected are pushed. The resulting aligned graph gives a very good representation of the major semantic fields present within the text. Furthermore, community detection algorithms are applied to the resulting network, sorting the nodes (words) into the different groups according to how interconnected they are to one another. Every community is represented with a different color. As a result, if two words co-occur often together inside the same text they will be positioned next to each other on the graph and also belong to the same community (and, thus, have the same color on the graph). These communities represent the topics inside the text. Finally, the nodes are ranked according to their betweenness centrality measure: the bigger the node, the more different communities it belongs to.
It’s worth noting that such approach is very different from so-called “tag clouds”. Tag clouds show the most frequently mentioned words and they rarely position these words according to their proximity within the text. Therefore, one can get a general idea of the vocabulary inside the text, but it’s very hard to have a sense of the meaning that is produced using this vocabulary. Text network visualization, on the other hand, emphasizes both the most frequently mentioned words, as well as the relationships between them, making it much easier to understand what the text is about. Furthermore, it can also detect the topics inside the text, making it a much more useful tool for improving text comprehension and providing a much more useable interface for text navigation.
Bush, 2001:
Bush, 2005:
Quite a generic agenda at first sight, however,
Bush was the first one to introduce the notion of “time” and use it to motivate
certain policies. It’s all about the Now: “In all of these ways, I will bring
the values of our history to the care of our times.” Not surprising that the
“story” is also such an important concept in his speech: it’s full of short
stories. In 2005, after the re-election is over, Bush is running the second
term, probably thanks to his emphasis on “freedom” and “liberty” – a trick that
always worked in the US and that was successfully employed by Reagan in his
second term (see above).
Obama, 2009:
Obama, 2013:
The master of rhetorics, Obama combines the best of his predecessors in this inauguration speech. No wonder the “word” has such high relevance in his speech – it refers to the moments Obama is quoting someone else. In 2013’s speech the “time” and “require” probably relates to the fact that Obama had to respond to all the criticism that something had to be done immediately about the state of US economy and politics – and he successfully addressed these concerns.
Source:
See more, about the text network analysis:
http://noduslabs.com/research/pathways-meaning-circulation-text-network-analysis/
Friday, 15 February 2013
Connecting the Community
We all live
in multiple on-line communities, but what do these communities look like? Where
are we located in each of our communities, and what role do we play?
The diagram
below shows an actual on-line community [OLC]. Every node in the network
represents a person. A link between two nodes reveals a relationship or
connection between two people in the community -- the social network. Most
on-line communities consist of three social rings -- a densely connected core
in the center, loosely connected fragments in the second ring, and an outer
ring of disconnected nodes, commonly known as lurkers. Communities have various
levels of belonging -- each represented by one of these rings. You may belong
in the core of one community, while being a peripheral lurker in another.
In the
above diagram, we see three distinct types of membership in our community --
designated by blue, green and red nodes. The proportion of nodes in each ring
in this population is fairly typical of most on-line communities -- the
isolates [lurkers] outnumber the highly-connected by a large ratio. The outer
orbit in the network above contains the blue nodes. They have been attracted to
the OLC, but have not connected yet. The blue nodes contain both brand new members,
who have not connected yet, and passive members who have seen no reason to
connect. The passive group is the most likely to leave the OLC, or remain as
absorbers-only of the content in the community.
The green
nodes have a few connections -- usually with prior acquaintances. They are not
connected to the larger community -- only to a small, local group. They do not
feel a sense of true membership in the larger whole, though they may identify
with it. The small clusters of friendships amongst the greens can be maintained
by other media and do not need this particular OLC to survive. They are also
likely to leave or become passive and will likely do so in unison with the rest
of their small circle of friends.
The inner
core of the community is composed of red nodes [zoomed-in view below]. They are
very involved in the community, and have formed a connected cluster of multiple
overlapping ego networks. The leaders of the OLC are embedded in this core
cluster. The core members will stay and build the community. Unfortunately they
are in the minority. The core node consists of usually less than 10% of most
on-line groups -- sometimes they are as few as 1% of the total OLC. Although
small, they are a powerful force of attraction. It is the core that is committed
and loyal to the OLC and will work on making it a success.
Online
communities and social networks are often conceived and developed by businesses
and organizations that focus on: "How can we use the online community to
benefit us?" Focusing only on how to utilize the community, leads many
organization to failure in building these communities! They fail at community
development by not creating a strategy that makes sure their target audience is
gaining a positive experience and practical benefits from participating in the
community. It is amazing how many organizations try to build on-line social
networks while ignoring the needs of the very people they are trying to attract
and influence! It is then no surprise when large chunks of their target group
leave when the "next big thing" comes around:
SixDegrees-->Friendster-->Orkut-->MySpace-->Facebook-->Next? To
build a vibrant and growing OLC, you need to support natural human behavior,
not work against it. You need to think sociology, not just technology.
The field
of social network analysis [SNA] gives us tools to both know the net and knit
the net. SNA maps and measures the paths of information, ideas and influence in
the community. SNA reveals the emergent patterns of interaction in
organizations and communities and allows us to track their changes over
time.Growing a community is not just adding new members. It requires adding
both people and relationships -- nodes and links. Node counts are important in
social networks, but it's the relationships -- and the patterns they create --
that are key! A community thrives by its connections, not by its collections!
It's the relationships, and the prospect of future relationships, that keep
members active and excited.
Wednesday, 13 February 2013
Manchester City vs Liverpool: Passing network analysis
At the
beginning of February, Manchester City drew 2-2 with Liverpool at the Etihad,
so a football loving blog decided to take a look at the match from a network
point of view, resulting in the following research. We have already reported
about something similar regarding basketball.
The
positions of the players are loosely based on the formations played by the two
teams, although some creative license is employed for clarity. It is important
to note that these are fixed positions, which will not always be representative
of where a player passed/received the ball. Only the starting eleven is shown
on the pitch, as the substitutes weren’t hugely interesting from a passing
perspective in this instance. Only completed passes are shown. Darker and
thicker arrows indicate more passes between each player. The player markers are
sized according to their passing influence, the larger the marker, the greater
their influence. The size and colour of the markers is relative to the players
on their own team i.e. they are on different scales for each team.
In the
reverse fixture, Yaya Touré and De Jong were very influential for City but
Touré was away at the African Cup of Nations, while De Jong joined Milan shortly
after that fixture. Their replacements in this game, Barry and Garcia, were
less influential, although Barry had the strongest passing influence for City
in this match, with Milner second. The central midfield two, Lucas and Gerrard,
were very influential for Liverpool and strongly dictated the passing patterns
of the team. They both linked well with the fullbacks and wider players, while
Lucas also had strong links with Suárez and Sturridge. Certainly in this area
of the pitch, Liverpool had the upper hand over City and this provided a solid
base for Liverpool in the match.
Similarly
to the Arsenal game, Liverpool showed less of an emphasis upon recycling the
ball in deeper areas. Instead, they favoured moving the ball forward more
directly, with Enrique often being an outlet for this via Reina and Agger.
Liverpool’s fullbacks combined well with their respective wide-players, while
also being strong options for Lucas and Gerrard. Strurridge was generally
excellent in this match and was more influential in terms of passing than in
his previous games against Norwich and Arsenal, combining well with Suárez,
Lucas and Gerrard.
At least
based on the past few games, Liverpool have shown the ability to alter their
passing approach with a heavily possession orientated game against Norwich,
followed up by more direct counter-attacking performances against Arsenal and
Manchester City. The game against City was particularly impressive as this was
mixed in with some good control in midfield via Lucas and Gerrard, which was
absent against Arsenal. How this progresses during Liverpool’s next run of
fixtures will be something to look out for.
Tuesday, 5 February 2013
Basketball Isn’t a Sport. It’s a Statistical Network
Team sports
and statistics are no strangers, take sabermetrics, that revolutionized game
analysis for baseball, while making it more fun to watch. The story might sound
familiar if you have seen Moneyball, where Brad Pitt took on the role of Billy
Beane, who pumped up the game of the Oakland A’s.
Compared to
baseball, though, basketball is much more dynamic, and ball movement becomes a
key variable in success. Passing is one of the fundamentals of hoops, and in
the upper ranks of the sport, turnovers — often the result of wayward passes —
contribute to ticks in the win-loss column. Fast, agile passing can make or break
a team. That’s why sabermetrics might not tell the entire story about what
happens on the court. Researchers at Arizona State University, led by life
science professor and basketball fan Jennifer Fewell and math professor Dieter
Armbruster found an ideal model to explain the results of the 2010 NBA playoffs
by simply keeping their eye on the ball. Their work opens the door to an
entirely new line of sports analysis, from game-tape breakdown to highlight
reels and augmented-reality visualizations.
Their
method - not surprisingly – was network analysis, which turns teammates into
nodes and exchanges — passes — into paths. From there, they created a flowchart
of sorts that showed ball movement, mapping game progression pass by pass:
Every time one player sent the ball to another, the flowchart lines
accumulated, creating larger and larger and arrows. Using data from the 2010
playoffs, Fewell and Armbruster’s team mapped the ball movement of every play.
Using the most frequent transactions — the inbound pass to shot-on-basket —
they analyzed the typical paths the ball took around the court.
Network
analysis of the Chicago Bulls, showing the majority of ball interaction
remained with the point guard. Image:
Network
analysis of the Los Angeles Lakers shows the team is far more likely to
distribute the ball among more players, using the “triangle offense.”
For most
teams, the inbound pass went primarily to the point guard, generally a team’s
best ball handler. But point guard-centric, such as the Bulls, didn’t fare well
in the 2010 playoffs, the researchers told Wired. On the other hand, the Los
Angeles Lakers — which won the 2010 NBA championship — distributed the ball
more evenly than their rivals, embracing what Phil Jackson calls the “triangle
offense,” a technique pioneered by Hall of Fame coach Sam Barry. The basic idea
is simple: Maintain balanced court spacing so any player can pass to another at
any point.In their
model, Fewell and Armbruster found a mathematical explanation for why the
triangle offense works — the point guard was no longer the only player feeding
passes to fellow players; his teammates were just as likely to take on that
role. With more potential passers, there are more potential paths for the
opposition to defend.
To quantify
their results, published in the journal PLOS ONE, the researchers derived the
entropy, or measure of system disorder, for each team during each game. In six
of the eight first rounds, winners had higher team entropy, and therefore more
randomness, than losers. Though the sample size of teams in the NBA playoffs
may be small, the data suggest a possible relationship between quick, unpredictable
ball movement and success in games.
While fans
direct cheers that fill sports arenas toward athletic giants such as LeBron
James or Kobe Bryant, bright statisticians still sit in the shadows. But when
these mathematical stars begin helping LeBron improve his game, it’s certain
they’ll hear more and more of the applause.
Hungry for more? Read the full article on Wired.
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