Gaming has become the most popular form of entertainment, and in today’s time, there are so many different and unique ways to experience it. There are many different gaming consoles to play on, and many different platforms to broadcast your experience, one of them being Twitch, the most commonly known and popular live streaming service.
Twitch streamers can broadcast their games to viewers
in real time to entertain, much like watching television or a movie. Viewers
can even chat with the streamer, as well as donate money to show their support
for the stream. Additionally, viewers can follow streamers to not only show
support, but to be notified when they go live, similar to how people subscribe
to YouTube channels.
The growth of a Twitch streamer can be shown to follow Power Laws, which state that in a network, majority of common nodes have very little connections, and few important nodes have a lot of connections. This is exactly how a twitch streamer grows in popularity, as the average viewer may follow a streamer, which increases the connections the streamer has, but the connection between that average viewer to anyone else has not changed.
In this picture, suppose node A is a twitch streamer, and nodes B – F are viewers that follow streamer A. Even though B – F are following streamer A, they can interact with each other through the community that has been built, and in this picture, B and C are connected. This is also seen through viewer X, where X is connected to A and became a part of this community, and ended up connected to B.
Overall, Twitch is more than just a website to watch a person broadcast themselves playing a video game. It is a community of people who share the same passion, and the larger the streamer is, the larger their built up community is as well.
As the rapid development of social media especially in the mobile platform, various homogenous social application has been developed and widely being used(Instagram, Twitter, Facebook, e-mail). Users can share information on the web anytime, anywhere, resulting in large amount of user data. Social network actually have a lot of practical value under the era of big data. Therefore, the data in many fields can be detected by the special instruments such as police departments.
Person of Interest is an American science fiction crime drama[1] television series that aired on CBS from September 22, 2011,[2] to June 21, 2016,[3] its five seasons comprising 103 episodes.
The classic drama PERSON OF INTEREST tells a story that using a backdoor of a “machine” to detect the possibility of the happening of crime, and figure out who might be the potential victim or criminal. The mechanism beneath the “machine” is the analysis of the big data comes from social network of the victim or criminal, for example, if a married man has a mistress, they will try to text each other, the information of their communication will express what are they thinking and planning, so the wife of the married man might become a potential victim.
The big data from personal social network can be used to:
To reveal hidden relationships, detect any criminal patterns, and prevent security threats.
Link personal real-time social account to discover unusual user behaviour and suspicious transactions to expose fraud base on the his historical account activities.
Test new sources of information and various data as evidence of criminal activity, such as the Internet, mobile devices, transaction processing, email, and social media.
In 2010, LAPD (Los Angles Police Department ) become the first employ data technology and information about past crimes to predict future unlawful activity. The technologies they were using are designed to predict where and when the crimes are likely to occur in next 12hours by examining the 10 years of data.
The Marvel Cinematic Universe is one of the most successful franchises in the world, earning billions but what about other more unknown cinematic universes. I am not talking about well known cinematic universes like the DC cinematic universe nor the Star Wars cinematic universe but rather a several clusters of films and television shows that are all unique with different actors and directors that become interconnected narratively through the roles and stories in these mediums. These cinematic universes can be viewed as a graph where each movie/show is a node on the graph whereas the edges would be the narrative threads or characters that connect the films together.
An example of this is would be the United States Space Program cinematic universe (USSPCU) which contains the films The Right Stuff, First Man, Hidden Figures, and Apollo 13. The Right Stuff is a film made in 1983 about the first 15 years of the NASA space program and has an actor who portrays Gus Grisson who we see him become Mercury Astronaut his character connects to both First Man and Apollo 13. In the 2018 film the First Man which follows Neil Armstrong and the Apollo 11 mission, we are reintroduced to Gus Grisson who becomes an Apollo 1 pilot who died during one of its launch tests. Both of these films are connected to the 1995 Apollo 13 film through a brief scene where the Apollo 13 astronauts meet Neil Armstrong and a key conversation between Jim Lovell (portrayed by Tom Hanks) and his son about the dangers of their mission which mentions the death of Gus Grisson. Another connection is the character Deke Slayton who is an astronaut recruit in The Right Stuff who becomes an executive who oversees the NASA space program in The First Man and Apollo. Hidden Figures, a 2016 film can be seen as a side story connected The Right Stuff overseeing the Mercury 7 launch which also shares 4 characters with each other. These links show how films can unintentionally become interconnected by the story or narrative threads and can tell a multi-movie story that we might not otherwise see to create a cinematic universe. Films based on historical events such as the USSPCU is an easier example to find as many of these historical pieces can be linked through real people that the actors’ portray. However, there are two other theories that feature only fictional entertainment to create new universes.
There is a theory that is based on television shows, it is called the “The Tommy Westphall Universe” (refer to the image below), which states that there are more than 400 interconnected TV shows and that they are all part of a dream from the character named Tommy Westphall, who is from a TV show called St. Elsewhere. This universe was constructed through the various crossovers and spin-offs with St. Elsewhere and other shows thus creating an ever-growing universe where any show that collaborates with anyone shows from the existing universe would then be added to the overall universe.
The other theory is the Pixar Animated Cinematic Universe which states that each Pixar Movie is related through several easter eggs that include cameos from other franchise characters such that they can create an overarching narrative.
Hughes, William. “Do Historical Movies about the Same Event Count as the ‘Original Cinematic Universes’?” AV Club, AV Club, 23 Nov. 2018, news.avclub.com/do-historical-movies-about-the-same-event-count-as-the-1830625342.
Luling, Todd Van. “Fan Theory Proves Almost All TV Shows Exist Within Same Universe.” HuffPost Canada, HuffPost Canada, 16 Sept. 2015, www.huffingtonpost.ca/entry/tv-show-fake-tommy-westphall-universe_n_55f84ba1e4b00e2cd5e8118a?ri18n=true.
“The Pixar Theory an Interactive Story.” The Pixar Theory, 97th Floor, www.pixartheory.com/.
Blizzard has been a mainstay in the video game industry for 19
years creating games which were massive successes and which have spanned
through generations including titles such as Warcraft, Diablo, Hearthstone, and
most recently Overwatch. Although Blizzard Entertainment has been a giant in
the video game industry for 19 years, the company has not held a spotless track
record, and the company is continuing to add blemishes to their reputation as
time goes on.
Earlier this October, controversy struck as Blizzard disciplined a player for speaking up on the recent political events happening in Hong Kong. During a tournament, a player was interviewed after a match and brought awareness to the Hong Kong protests by donning a ski mask and gas mask and stated “Liberate Hong Kong. Revolution of our age.” This put Blizzard Entertainment in a difficult position due to their ties with China as Tencent, a giant in the Chinese gaming industry, owns a stake in Blizzard and the relations between China and Hong Kong are obviously not great at the moment. Blizzard responded with a harsh disciplinary action of removing all earnings from the player, as well as banning the player for a whole year. Additionally, Blizzard punished the two commentators giving the interview by immediately dismissing both of them. This has led to an uproar within the community against Blizzard for their actions which draws a parallel to the Balance Theorem.
Prior to all of the controversy and prior to the political
disagreements between China and Hong Kong there was relative balance between
Blizzard, China, Hong Kong, as well as the community. If represented by a
signed graph, it would be a complete signed graph with only positive edges.
However, China and Hong Kong has had conflicts between each other which
disrupted the balance between the four parties. This has led to the divide
where Blizzard sided with China and the community siding with Hong Kong to form
two parties and it has partitioned the group.
However, Blizzard has recently taken a step back on the disciplinary actions on both the player and both commentators so it remains to be seen if the parties involved will in fact make peace with one another or if there will be a permanent divide caused.
Social media has change the way we all interact with each other across the world. It can be many different types of media from text to image, and even touch. It can also cover many different ranges from across the world to you’re friend sitting next to you. This flexibility is one of the traits which gives social media this appeal as it creates a piece of the internet for everyone.
The internet was initially created without security in mind – internet was a better place, it was a simplier place. As the internet grew, malicious intent grew through virus and exploits which resulted in countermeasures such as encryption. Malicious intent can be “physical” like DOS attacks and leaking of passwords, but it can also be social like astroturfing attacks.
The acts of astroturfing vs real widespread movement represented by real grass and artifical turf
Astroturfing is “the attempt to create an impression of widespread grassroots support for a policy, individual, or product, where little such support exists”. In layman’s terms, we are creating a “movement” for something through the seeding from a mass of people. The attack is similar to DOS, where a set of hacked or created accounts are used to post about a certain topic. It is becoming a very widespread and effective means of advertisement, but manipulates the social media leading to skewed impressions and misrepresentations.
Relating this back to graph theory, using graphs to represent different characteristics we are able to visualize relationships between these astrobots. In the diagram below, the edges represent a keyword relationship between astrobots, which are nodes. From this picture, we can see different communities from the clusters with represent different types of account, such as suspended accounts, used to as a bot. By using a signed colored graph, where a positive sign is given to nodes of a similar type and negative otherwise, we are able to develop communities in relationship the keyword attacks.
Graph representing the relationship between astrobots via attacking the same keyword on the same day
Countering astrobots can be a very hairy situation, since depending on the type of bot, there can be side-effects from actions such as suspsension and/or banning. For example, these bots can be affected without the user’s acknowledgement, therefore suspension would led to anger or fustration from the original owner. By using graph theory, we are able to learn about the motives and characteristics of astrobots from different perspectives. Using this information, we are able to prevent this from happening and apply the most appropriate action without affecting actual users.
References
Elmas, Tuğrulcan, et al. “Lateral Astroturfing Attacks on Twitter Trending Topics.” ArXiv.org, 17 Oct. 2019, https://arxiv.org/pdf/1910.07783.pdf.
An interesting article posted in June of 2018 indicates that the power law can be observed in a number of forms of media, with some displaying a steeper curve than others. It is interesting to observe these distributions that can be modelled with math exist in the real world. For example, observe how steep the graph showing success of video game publishers is:
The success of video game publishers is measured by their total global sales, and it is clear that that a select few have lots of success while the rest have very little. In comparison, other forms of media are not as punishing to those less successful, like with song artists:
The curve here is far less steep, indicating that it is easier to get one of your songs to stay on the Billboard Hot-100 for a couple weeks than it is to be a successful video game publisher.
The article also discusses the 80-20 rule, also knows as the Pareto Principle, and looks at how much success the top 20% of entries for each industry have.
It should be noticed that these numbers are related to the steepness of the graph, as one can see that the top 20% of game publishers own 97.69% of all the success in the industry, hence the extremely steep curve. On the other hand, the top 20% of songs only hold 38.88% of the overall song success. It was no accident that I chose those two specific charts earlier in the article. In fact, the tail of the songs chart actually does not follow the power law, which make it even more interesting that out of all the forms of measuring success in the media industry, the number of weeks a song stays on the Billboard Hot-100 is the only one to not follow the power law distribution. Some industries also follow the 80-20 rule very closely, such as success of movie directors:
This curve looks far more like what I expect to see when I think of the power law.
This obviously relates to the entire discussion of the power law from lecture as well as whether or not success depends on simply being lucky or being first, as the information above would suggest that early success is very important in the world of media, although less important for songs than it is for video game publishers.
I chose to write about this article because most of what we see in today’s world is a product of the media industry, and so I thought it was interesting to see how closely success in this industry follows the power law distribution, as likely no movie director or book author is thinking about it when creating their work, and yet it shows up anyways. We were shown an example of this in class with the experiment carried out, but this is a far more recent look of the topic, and I thought it was worthwhile to post.
“We cannot stop natural disasters but we can arm ourselves with knowledge: so many lives wouldn’t have to be lost if there was enough disaster preparedness.” – Petra Nemcova (4)
The world has finally begun to act in preventing climate change. Unfortunately, the damage has already been done, and what are defined as natural disasters have seen a dramatic increase within the past 50 or so years (1).
Something that I know for a fact I take for granted, and I’m sure many others do as well, is our infrastructure that we have within Toronto. Such infrastructure includes: our road systems, power grid, drainage systems, sewage system, internet lines, and many more. Thankfully, Toronto is situated in a very key geological region where we do not see many natural disasters such as hurricanes, floods, or forest fires; but many highly densely populated areas of the world are in these danger zones (2).
One of the reasons as to why these disasters can become so catastrophic in terms of monetary damage, and for the local population, is due to the effects that they have on the local infrastructure. There are times when even a failure of a key piece of infrastructure can cause such an event – such as a dam breaking (3). Without access to this infrastructure, locals are unable to deliver aid to the afflicted, or even worse, notify residents of certain events that can put them in danger or affect them in some way.
Thankfully, the local population usually has some kind of remediation plan set in place that would help mitigate the damage. Although these infrastructure systems are built to be resilient, they are not immune.
How does any of this relate back to what we’ve been discussing within CSCC46 you may ask yourself? As we’ve already explored, infrastructures such as the internet can be treated as a network. I will now introduce a network that has not yet been covered by the course: a lattice network. You may think of it as a matrix where each index is a node that has edges connecting to its direct index neighbours, as well as, the index one level above and one level below. In mathematical terms we can conceptualize this as the following: For a given nxn matrix A, an index a_(i,j) has an edge to the following set {a_(i, j – 1), a_(i, j + 1), a_(i + 1, j), a_(i – 1, j)}. Back to the original question, many infrastructure systems can be plotted using such a lattice network, think of road systems, sewage systems, or power grids as examples.
When speaking about such a lattice network, a natural disaster has the capability of breaking such a lattice structure, thus disabling the local infrastructure. Such an event is called a localized attack. I came across a very interesting article (Inspiration) that explored recovery assessments of such networked infrastructure under such an attack. Understanding the best way – in terms of efficiency, cost, and possibly human lives – to repair such a network can prove to be quite useful when it comes to planning ahead. This ‘best way’ is what is referred to as resilience.
The article explores different recovery strategies that would be able to recover from such an event, as demonstrated in the image below. The Periphery Recovery strategy targets the most populous nodes – in terms of people served – before going to repair the attacked node – we will call this the root node. The Preferential Recovery strategy targets nodes that that have edges leading to the most populous nodes. And lastly, the Localized Recovery strategy that assigns a certain max weight w to the root node, and then some weight w – d where d is the distance away from the root node and basically works in a Dijkstras’ fashion to repair the root node as the target in hopes of restoring the full network.
The article further explores something called the resilience metricΦ which is used to analyze the resilience of a network given some localized attack and can be used to create a resilience-based optimization model – using the strategies listed above – in comparison to some existing network.
An example was given where a water distribution system was used to demonstrate such an analysis. Meant to stop propagation of issues resulting from the failure of the water distribution shortage and to bring back the system in the most efficient way to ensure its resilience is maximized.
I won’t bore you with the math, but this was simply a small look at the potential that network analysis can have to strengthen existing infrastructure and understand how to maximize it’s resilience towards localized attacks which can be the difference between life and death during a natural disaster.
I end this blog post with some parting thoughts. Our infrastructure that we’ve put in place is responsible for getting you your water, your heat, your electricity, that most of us take for granted. If such a localized attack were to occur as result of a natural disaster, or other means, our number one priority should be in restoring this network to ensure it has no further cascading negative effects on it’s populous.
Inspiration: Afrin, T., & Yodo, N. (2019). Resilience-Based Recovery Assessments of Networked Infrastructure Systems under Localized Attacks. Infrastructures, 4(1), 11.
Citations:
Hannah Ritchie and Max Roser (2019) – “Natural Disasters”. Published online at OurWorldInData.org. Retrieved from https://ourworldindata.org/natural-disasters
Dillinger, Jessica. (2018, September 26). Countries Most Prone to Natural Disasters. Retrieved from https://www.worldatlas.com/articles/countries-with-the-deadliest-natural-disasters.html
CNN Wire Staff. (2010, July 24). Dam fails in eastern Iowa, causing massive flooding. Retrieved from http://www.cnn.com/2010/US/07/24/iowa.dam.breach/index.html
Petra Nemcova Quotes. (n.d.). BrainyQuote.com. Retrieved October 24, 2019, from BrainyQuote.com Web site: https://www.brainyquote.com/quotes/petra_nemcova_426837
FC Barcelona is a well known soccer club which plays in the Spanish League La Liga and during the 2009-2010 season they were unbeatable winning six major championships. You might wonder what their secret to success was during that season and if it is possible to analyze the aspects of their game better than existing methods. Can we analyze this using networks and concepts learnt in CSCC46? A recent study from Universidad Rey Juan Carlos proves that through using networks and analyzing it is possible to better understand the style of play.
First a network was created with nodes representing players in a particular game and an edge between each player existed if there was a pass between them at any time. The study also recorded the position of each player during the game. The network also contained a sliding window of 50 passes which will delete the very first edge as the game progresses and this helped in analysing different phases of the game.
What did this network reveal about the team? The network showed central team players through which the ball frequently passes through , triads between players who frequently passed the ball between each other and more. Calculating the clustering coefficient of this network concluded that Barcelona had one of best passing between neighbours of its players among all teams. It was also decided that Barcelona had the best shortest average paths which again shows the skill of passing across the team. The study also predicted probabilities of the team scoring a goal given a particular network.
Here is a graph that shows properties of the network from the study.
It is interesting how a network of a sports team can reveal a lot more about the style of a team and I wonder what the potential uses of it are in the future. Maybe opposing team coaches can use this information to better tackle a strong team or to use some of the techniques and strategies in their own team to improve.
The existence of the power law has been witnessed in various networks created by humans in various forms as discussed in class. As previously discussed it shows in various social constructs such as social networks and even actor collaborations, however the power law also makes itself apparent within nature and in our planet. What is even more interesting however, is that the power law exists to even a scale as large as celestial bodies, from Pluto, to our moon and even the sun itself, the power law is everywhere.
One study looks into the the craters in Pluto, upon the recent high resolution pictures of Pluto, scientists were able to look at the actual craters in the planet. It investigated 87 craters in Pluto, ranging from sizes of 0.87km to 37.77km. It was found that there was a scaling coefficient of “α = −2.4926 ± 0.3309”, with a “Dmin = 3.75 ± 1.14 km” (or xmin if we use the formula we discussed in class). Upon values of distance D within the range “[3.75 ± 1.14 km, 37.77 km]” there was a log likelihood (L) = 104.5688. It is incredibly interesting that these scientists are using the same formulas we are to calculate the scale and witness power law on a celestial scale!
Below indicates a segment of craters within Pluto that were analyzed for their crater size
The study then continued to look at how these craters were very comparable to power-laws in other celestial bodies such as our Moon, Mar’s satellites Phobos and Deimos, and even the Earth! As an interesting tangent, the study also referenced how the power law existed in the relationship between the frequency of solar flares and total flare energy. Going on to provide other examples such as the distribution of initial masses for star populations, Kepler’s third law, and even to the scale of the distribution of galaxies in the universe! It is incredible to think of how this law shows itself throughout the universe. From human social constructs, things we made, to the scale of galaxies in the Universe!
You’ve might have read articles on the news or blogs about how dolphins are very similar to humans and they way they behave. Dolphins have a naturally friendly relationship with humans and many dolphins and human interactions have been amiable. Given their social aptitudes, many have wondered how they interact with each other. Dolphins can form friendships and form their own social networks in which they find comfort associating within. This also brings up the social hierarchy in the roles that each dolphin plays in their social circle and where each dolphin fits in within their miniature society. While dolphins can’t come in to do interviews or fill out surveys, David Lusseau and M. E. J. Newman analyzed these relationships to find measures of association and behavior of a group of bottle-nose dolphins in New Zealand.
HTWD1X Bottlenose Dolphin (Tursiops Truncates)
Lusseau and Newman started by determining the communities and modeled a graph of betweeness of an assemble of dolphins through Girvan Newman and then identifying the critical dolphins that keep the community together, and the result of these groupings that create homophily within the entire group structure. While the analysis of humans brings upon many concrete statistics that can be compared such as age, race, gender, occupation, etc, dolphin can only be measured in age (which was mostly estimated) and gender (which was observed).
This is their findings presented through a Girvan and Newman algorithm, finding and identifying communities. Males are represented as squares while females are circles. Ones that could not have their gender identified are triangles. In this diagram, they concluded that there 2 major communities, with 1 split into 3 sub communities. The black shaded group forms one community while all other nodes form another, splitting between smaller sub communities that interact within the their own group more frequently.
This graph shows the dolphins that the center of these communities and have a betweeness greater than 7.33 . These dolphins marked by the filled in shapes are the “brokers” between communities that will interact with other communities as well. They found that when sn100 was away, the 2 communities almost never interacted and drove the 2 groups apart.
We can see that even dolphins have exhibit homophily and will stick within their own group of friends when presented and it is interesting to see how close dolphins and us are alike.
References
Lusseau, D., & Newman, M. E. J. (n.d.). Identifying the role thatanimals play in their socialnetworks. Retrieved October 23, 2019, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1810112/pdf/15801609.pdf.
