Category: Uncategorised

As one of the most violent cities in the USA, Kansas City realized that it needed to take a different approach to solving it’s crime problem. So, under new government, the Kansas City No Violence Alliance (aka KC NoVA) was created. KC NoVA decided to approach the problem from a network perspective and made the following assumptions:

1. People with friends who have committed crimes, are more likely to commit crimes
2. The closer you are to violence, the more likely you are to be a victim of it
3. Violence is concentrated among groups of people

The first and third assumptions in particular highlight Granovetter’s model of networks, with violent crime primarily occurring in these highly connected clusters of people.

Using these assumptions, their first order of business was to graph the relationships between the gang members.

The connections between people are determined based off of a multitude of different metrics such as traffic stops, arrests, informants information, street intelligence, and more. Using real world information in combination with analysis of the graph, they were able to determine the key players in the gang.

Taking into account whether the individual was on probation or parole, and their betweenness on the graph, police selected two people from each group to reach out to. They warned the individual about violence, and offered targeted support for education, employment, anger management, etc. This method allowed the city to efficiently allocate it’s limited law enforcement resources, and was successful, leading to the number of homicides dropping by more than a quarter next year.

What stood out to me about this article was the proactive approach to crime reduction. Rather than simply reacting to crime, the city was getting out in front of it, not only saving the lives of the would be victims, but also that of the perpetrator. It is truly a win-win scenario and highlights the practical implications of some of the more nebulous concepts of graph theory.

Relevant Links

• KC NoVA Website
• Using SNA to reduce violence in Kansas City: Past Present and Future
• Fight Crime with Social Network Analysis

Recently in one of my Social and Information Network lectures, we discussed the idea of Structural Balance. This was essentially the idea that if a network had signed edges (positive or negative), then we could classify the network as stable depending on the patterns every triangle that was made with the signed edges made.

Since the usual example that was used for our networks was social networks, it was easy to see this representation as a social network of people, where the signed edges either meant two people liked or hated each other. In that case, a triangle with only one positive edge would represent two friends who both hate the person, and a triangle with only one negative edge could represent a love triangle, where two people are competing against each other for the love of the other person.

When I pointed the love triangle case out in class, I got a few laughs. The professor ended up stating that the love triangle case is also not stable. Although it is strange and possibly funny, I do find it interesting that we could use graph theory, and other course content that we learnt in class, to analyze the different structures that may come up when analyzing relationships in a drama for example. Maybe it’s possible to analyze a drama episode by episode, mapping out the relationship network to predict interpersonal conflict between the different characters depending on the stability of the triangles. Since love triangles are a common plot device, maybe you could even use this to predict what happens next episode, if the negative and positive edges indicate the intensity of the relationship between two nodes.

I then decided to search around for articles around this concept and found this one. It’s not super recent (2015), but here they trained a neural network on the TV show “Friends”, (the scenes and the subtitles). From that, they were able to generate this affinity chart.

Admittedly, I’m not 100% sure of how they generate this chart, but it’s neat that tracking this is even possible. I wonder if this can also be used to predict how countries interact with each other. I did find another paper that measures international relations by the use of social media, but given the use of bots on social media, I decided not to look further into it.

Networks and their analysis is one of the core topic of this course for network analysis can prove useful and powerful in various fields such as biology, psychology, computer sciences, social sciences, etc. During the lectures, we saw how network analysis on Facebook data (or any other social media platform) to can help us gain knowledge about friendships and characteristics of social circles on the platforms. We also saw how networks and strong/weak ties can provide us with meaningful insights on job searching/hunting. These are just few of the many uses and applications of the network analysis. Through this blog, I would like to share an interesting application of network analysis which I came across. It is “Combining network analysis with epidemiological data to inform risk-based surveillance: Application to hepatitis E virus (HEV) in pigs.”

This interesting research was conducted by Morgane Salines, Mathieu Andraud and Nicolas Roseab. The goal of the research was to “evaluate the impact of animal movements on pathogen prevalence in farms and assess the risk of local areas being exposed to diseases due to incoming movements (the transportation)” and they combined Network Analysis with epidemiological data to achieve the same. They collected data consisting of pig moments from farms, slaughterhouses, etc. for the period of a year (January – December 2013) from the French Ministry of Agriculture.

            Now comes the interesting part of how they designed or defined a network or a graph. They modelled the data into directed networks which were aggregated on the data for the entire year. The nodes of the network represented the pig holdings, i.e. the farms, slaughterhouses, trade plants, etc, while the movements (all the transportations) between them were represented as directed links. They then calculated various centrality measures such as in-degree, out-degree, betweenness, etc for 178 farms. The graph below shows a sample network where U stands for Unloading pigs and L for loading pigs.

After the preliminary work was finished, the researches then conducted “a univariable analysis to assess the statistical link between each explanatory variable (i.e. the farms’ centrality metrics) and the outcome (i.e. the unbiased within-farm HEV seroprevalence).” PS – HEV seroprevalence was defined as the “number of HEV-seropositive pigs in relation to the total number of pigs sampled in the farm.” After this analysis, they successfully plotted the risk indicator for each of the French departments which looked like this.

Thus, it can be concluded that simple network (and statistical) analysis along with epidemiological data can effectively help the surveillance programs by indicating the risk and thus helping prevent diseases. The network they modeled was very simple and was something that we learnt in the C46 class.

REFERENCE –

MorganeSalinesab, MathieuAndraudab, & NicolasRoseab. (2017, November 20). Combining network analysis with epidemiological data to inform risk-based surveillance: Application to hepatitis E virus (HEV) in pigs. Retrieved from https://www.sciencedirect.com/science/article/pii/S0167587717305937.

In today’s society, people make connections with others from all kinds of backgrounds. Whether it be for business or hobbies, having a connection to share with will provide an individual with extra insight and empowerment. Therefore, it is imperative to learn effective networking skills to expand their social circle. What Does “Mutual Friend” Mean? and Psychological tricks to make people like you immediately are articles that explores the advantages of having mutual friends to establish friendship.

These article lists out 4 areas where mutual friends help in making connections: Identification, Social Networks, Dating and Mutual Friend’s Companions. To summarize, all areas make use of mutual friends to assure the new connection that you have similar traits as your mutual friends and provide a possibility of repeated encounters.

For example, Charlie has known Bob for a long time and talks to Alice on the daily. at some point in time, Charlie will have overlapped time spent with them both. Normally, 2 strangers do not engage in conversation but since Alice and Bob have a mutual friend, they both assume that the other have likable traits and begin to put effort in getting to know each other.

Interestingly, this relates to Social Network’s Triadic closure. Since your mutual friend knows both you and the new connection well, you both are very likely to have a weak or strong connection.

Relevant Links:
Psychological tricks to make people like you immediately
https://dailytimes.com.pk/46941/psychological-tricks-to-make-people-like-you-immediately/

What Does “Mutual Friend” Mean?
https://oureverydaylife.com/mutual-friend-mean-10031289.html

The topic of false and distorted news has been a part of our history for thousands of years. The underlying motive behind the spread of false information is to sway the opinion of people to achieve some goal. In recent times, the term “fake news” has made its way into common everyday vocabulary due to its prevalence in Western culture, particularly in Western politics where certain groups defame political parties to influence the outcome of elections. The cascading effects of fake news in society is extremely relevant to our course material as it is a topic that can be examined through the flow of information networks to understand how fake news becomes viral. This blog post will discuss the following questions: How exactly does fake news spread? Is there a way for social media platforms to eliminate the spread of fake news?

Fake news is spread most effectively through social media platforms, such as Facebook, Twitter, and Instagram. The spread of fake news is typically done through bots, and “According to an estimate in 2017, there were 23 million bots on Twitter (around 8.5% of all accounts), 140 million bots on Facebook (up to 5.5% of accounts) and around 27 million bots on Instagram (8.2% of the accounts)” for a total of 190 million bots on the three platforms combined. The bots reach a large audience by flooding the platforms with false information through trending topics or hashtags, to gain publicity on their posts by being recognized by the platform’s relevance algorithm. With countless bots simultaneously relaying the same false information, naïve audiences often tend to believe propaganda and further spread the lies themselves, creating a cascading effect.

Can social media platforms prevent the spread of false information? Miriam Metzger, a UC Santa Barbara communications researcher says, “Fake news is perfect for spreadability: It’s going to be shocking, it’s going to be surprising, and it’s going to be playing on people’s emotions, and that’s a recipe for how to spread misinformation”. The natural attractiveness of fake news to the human mind means that fake news will always manage to trend in some way. Platforms such as Twitter can use false information detection algorithms to help with reducing the flow of fake news by creating true and false information models and act on their platforms accordingly. However, the issue of fake news arising in social networks will never be eliminated entirely because of the emotional responses generated from them.

Relevant links:

https://www.vox.com/science-and-health/2018/3/8/17085928/fake-news-study-mit-science

https://www.cits.ucsb.edu/fake-news/spread

https://arxiv.org/pdf/1804.08559.pdf

https://www.cits.ucsb.edu/fake-news/brief-history

Zuck: Yeah so if you ever need info about anyone at Harvard
Zuck: Just ask
Zuck: I have over 4,000 emails, pictures, addresses, SNS
[Redacted Friend’s Name]: What? How’d you manage that one?
Zuck: People just submitted it.
Zuck: I don’t know why.
Zuck: They “trust me”
Zuck: Dumb f*cks
– Mark Zuckerberg, CEO of Facebook [1]

That quote is only tangentially related to what I’m going to talk about, but I feel it is not mentioned nearly enough. Social media has been largely blamed for the divide in the United States, and the rise of Nationalism all around the world. While some argue social media giants are either incompetent or complicit in the divisions they are causing, I feel it is far more likely a natural progression of business, social networks, and human nature.

=== It’s all about the Benjamin’s baby! ===

Thanks to draconian laws in the United States, money is an expression of freedom of speech, and companies count as people. In practice, this means Exxon Mobil, with its $30 million dollars in lobbying, gets more votes than you do (which is why the US still has a climate change denial problem) [2]. These allow the “motivated reasoners” to exist. They are people in a social network who have decided (for one reason or another) what they believe, and will not be swayed. They share information (blogs, new articles) constantly, and only share things that promote what they believe in. Since money is an expression of free speech, you can pay anyone to be a megaphone for what you want people to believe, and no one can stop you.
A less important note that is still related to money: social media companies are just that, companies. They need to push for profits, and we the people have spoken with one voice: “give it to us for free or we won’t use it.” As a result, we are the product, and Facebook sell advertisements. Social media needs to push for high user engagement, so they can sell ads that people will see. Alright that covers the business, let’s get to the fun.

=== Your taste is trash ===

As Mark is in need of a 3rd mansion, he needs the company to pad the bottom line. User engagement is the bread and butter, and social media companies have learned the golden rule of grabbing our attention: appeal to our negative emotions [3]. While news in movies will often be “local cat taken down from tree safely,” in reality, there has been a massive shift towards more negative, eye catching headlines. Because these are the article users are more reactive too, and engage most with, algorithms will push these types of news. It is just a natural progression, and not necessarily because they want to watch the world burn, although I wouldn’t put it past them.

=== It’s only natural ===

And now finally the source of this blog post:

When facts fail: Bias, polarisation and truth in social networks[4]

In this 2018 paper published by Cornell University, the researchers set out to find out why we end up so divided. They generated a random graph of “agents,” (people who send and receive information on the social network), with some set of edges connect agents to connect the whole graph. The model looks at a single, simplistic version of a statement, like “global warming is real and man made,” and each agent was assigned a 1 or -1 to start (at random), 1 representing agreement with the statement, -1 disagreement. Some percentage of the people were assigned “motivated reasoners,” they will not change their starting belief. The model then evolved the network overtime in discrete “timesteps”. At each timestep, every agent sends out a signal (1 or -1), to all their neighbours. A person’s belief is now based on a Bayesian representation of the signals they have received. The researchers let it run:

“Our results showed that in every case, motivated reasoners came to dominate the conversation, driving all other agents to fixed opinions, thus polarizing the network.”[5]

It is the only natural progression of social networks to make the truth unobtainable. If you ended up surrounded by motivated reasoners, you would believe whatever they tell you to believe. Keep in mind, real world networks are balance (either weakly or strongly), and in those networks, you already have an established web of trust with new sources or you friends. This is how you end up in an echo chamber, where you only hear opinions you agree with. The paper concludes:

“We show that this simple confirmation bias mechanism can generate permanent opinion polarisation. Furthermore, the model results in states where unbiased agents behave “as if” they were biased, due to their biased neighbours effectively functioning as gatekeepers, restricting their access to free and diverse information.”

A motivated reasoner doesn’t need to convince you, just someone in your coalition, and the network will do the rest for him. The algorithm puts coalitions into like minded groups, pushes dissimilar content to each, and thus a divide grows wider and wider. The algorithm increases engagement with more divisive content, the network owner gets richer, truth becomes perspective, and society falls apart at the seams.

Sources:

[1] Zuck-man-sam quote: https://www.businessinsider.com/well-these-new-zuckerberg-ims-wont-help-facebooks-privacy-problems-2010-5

[2] ExxonMobil wants to watch the world burn:
https://www.scientificamerican.com/article/exxon-knew-about-climate-change-almost-40-years-ago/

[3] Being sad is fun and profitable: https://www.pnas.org/content/116/38/18888

[4] When facts fail: Bias, polarisation and truth in social networks
By Orowa Sikder, Robert E. Smith, Pierpaolo Vivo, Giacomo Livan
26 Aug 2018
https://arxiv.org/abs/1808.08524

[5] Robert E. Smith Op-ed: https://www.azcentral.com/story/opinion/op-ed/2019/09/08/social-media-bias-blame-algorithms/2208612001/

Recently, a new malware known as Nodersok/Divergent, surfaced. While the steps this malware takes to infect a system are an interesting topic to look into, that will not be the focus of this post. Rather we will look at what an infected system can do, within the network it is connected to.

The malware allows malicious JavaScript code to run and execute under the valid program Node.exe. The payload of the malware contains basic functions, which turns the infected machine into a proxy, accessible through a remote machine controlled by the attacker. While this does not give the attacker direct access to other machines on the network, there is still a breach within the infected PC’s network. An attacker can for example, use that machine as part of other malicious activities. Since the requests are being proxied through the infected machines, they may not necessarily be traced back to the attacker, but rather from the infected PC’s. 

While the malware itself, turns machines into proxies, it is interesting to think about what security threats this may pose on its network, especially if this malware were to evolve. For example, take a machine that sits within an isolated network: or in terms of graphs, an isolated, disconnected SCC. Machines would only be accessible from other machines on the network. However, as soon as one machine becomes infected, it’s possible that the attacker now has access to information on other machines, as requests would come from the infected machine. 

This leads to an interesting idea of how networks should be set up, in order to protect against threats such as this. For example, is it important and necessary to have all the machines in one SCC, or can some parts be even more isolated? If communication channels between the machines in the network are two-way (such as in an undirected graph), a possible solution could be to have one way communications between machines, similar to a directed graphs, in order to restrict the way machines can communicate. All in all, security breaches from malware related to networking requests, can pose a large threat, and should be taken into account when designing how machines are connected within a network.

References
https://www.microsoft.com/security/blog/2019/09/26/bring-your-own-lolbin-multi-stage-fileless-nodersok-campaign-delivers-rare-node-js-based-malware/

The academic journal published by IEEE(Institute of Electrical and Electronics Engineers) proposed a new model to improve the quality of community discovery. This new proposed model addresses some of the limitations that traditional methods had and produces result with higher correspondence with the ground truth communities.

Traditionally, community discovery methods treat network structure as a static topology. They neglect the interaction between the information factor, as they only represent the possibility of interaction between users. However modern microblogging networks applications like Twitter, are extremly dynamic in content distribution and topological structure. The flow of information that is not considered in traditional methods could be applied to microblogging networks to better determine user interest community.

The proposed method uses multiple different data analysis algorithms to filter and integrate information interactions between users. It then uses machine learning strategy to analyze information to dynamically updates the characteristic of the network to cluster the community. 

This is interesting to me because, as we have learnt in class, sometimes data are lost in translation. This method takes some of the missing information into account and analyze them before translation. The resulting communities have higher correspondence and accuracy, which can provide more information than traditional methods.

Reference:

Jiang, Liang, et al. “An Efficient Evolutionary User Interest Community Discovery Model in Dynamic Social Networks for Internet of People.” IEEE Internet of Things Journal, 2019, pp. 1–1., doi:10.1109/jiot.2019.2893625.

For the past decade, the growth of social media platforms have been enormous. These platforms are used to connect and create relationships with friends and people that we interact with on a daily basis. Even though these social networks are meant for people to add their friends, it turns out that these platforms are very susceptible to attackers that are looking to send spam for their personal gain. But, how do these attackers get the opportunity to include themselves in a network of friends that they do not belong in? Using Facebook as a common social network, have you ever received a friend request from a random person? This is exactly the way these attackers get themselves involved within a network. All it takes is one friend of a large network of friends to accept that request from the attacker that will now allow the attacker a path to all other members of the large network. If you have any personal information such as your phone number or email on your profile, these will now all be compromised and make you vulnerable to spam attacks. Because of the undirected nature of these friend relationships, mindlessly accepting requests on these social networks will leave you exposed to such attacks.

If we think of this in terms of what we know about networks, this is an extremely common occurrence. Once a user forms a friend relationship with an attacker, this can be seen as a bridge edge from a group of attackers to a group of normal friends. Another aspect of social networks is the concept of mutual friends. This initial relationship between one attacker and one real user can quickly snowball into further relationships between other attackers and other real users. Therefore, we now no longer have a single bridge node connecting these two groups of graphs, but several local bridges that strengthen their relationships that will lead to more attacks.

In conclusion, being apart of a social network can leave us vulnerable from attackers looking to send spam messages. The obvious solution to this is to avoid including personal information in our social media profiles. Other than this, we must be careful with who we decide to share our profiles with, as we could be creating a bridge between a network of attackers and our group of friends.

Reference

Shrivastava, N., Majumder, A., & Rastogi, R. (2008, April 12). Mining (Social) Network Graphs to Detect Random Link Attacks. Retrieved from https://ieeexplore.ieee.org/abstract/document/4497457.

In last Monday’s class (30/09/19), we had discussed the notion of balance in graphs, following the reasoning of how relationships between friends and enemies would be structured in realistic scenarios. If you have a balanced graph, you can used known relationships to predict the relationships between nodes. For example, if you know that A is friends with B and B is enemies with C, it would be reasonable to guess that A is enemies with C as well. I thought that it would be interesting to apply this logic to a real-world situation and examine the relationships between nations. For this, I want to look at the ongoing Venezuelan Presidential Crisis. In short, the need to know is that there is a global debate regarding who is the rightful president of Venezuela between Nicolás Maduro and Juan Guaidó. Among the countries aligned with Maduro you have the likes of Russia, China, and Cuba. Of those supporting Guaidó, you would find the USA, Canada, Brazil, and the UK. Keep in mind that the full list of countries and their declared allegiances is much larger, but I just want to paint a general picture.

At the centre of the issue, you have the two Venezuelan parties, which can be comfortably labelled as having a negative relationship. We can also label the relationship between the Venezuelan parties and their respective supporters as being positive. This gives us two clear factions, one supporting Guaidó and the other Maduro. According to the logic dictated by balanced graphs, it would hold to reason that the countries within these factions would all have positive relations with each other and negative relations with countries in the opposing faction. This statement manages to hold in most high-profile cases, as shown in figure 1, with some examples being USA-UK or Russia-China, but there are some notable exceptions.

A prominent outlier I want to highlight is Canada-Cuba which have had very strong relations for decades. Despite this friendship, Canada and Cuba are in opposing factions regarding the Venezuelan Presidential Crisis. This Canada-Cuba relationship manages to create an unbalance in the graph, but would that strictly mean the relationship itself it prone to collapse? Over the recent months, I have not heard of any deterioration in the relationship between these two countries despite the clear difference in policy regarding Venezuela. Of course, it wouldn’t be surprising to read that there is an increase in tension behind closed doors, but as of right now, it doesn’t feel accurate to say that Canada-Cuba relations are in someway flawed. This relationship causes other issues with balance such as how Canada-Iran has a negative relationship, yet Cuba-Iran has a positive relationship.

Due to the Canada-Cuba example, I feel that it may be rather difficult to find a perfectly balanced graph using real-world data as the world is simply too intricate to be able to definitively say who are enemies and friends of whom. An important aspect to consider is that edges are binary, negative or positive. Relationships with countries are volatile and subject to change. An example is Brazil-Russia, notably in opposing factions, where their relationship has been improving, but are still in a tough position to gauge whether their current relationship could be described as friendly. Another thing to consider is a neutral relationship, such as the one between the UK and Cuba. Such a relationship couldn’t be expressed with graph balancing as each edge must be coloured as negative or positive, not allowing for neutrality of any kind. This is not to say that the notions of balanced graphs aren’t useful, but it may be more reasonable to look at an overall level of balance, such as relating to probability, as opposed to merely saying that the graph is balanced or unbalanced.

2019 Venezuelan Presidential Crisis Summary:

https://www.bbc.com/news/world-latin-america-48121148