Something about Social Network Analysis

When I first saw the sociogram which generated by our blogosphere, I am shocked, it’s so cool. The sociogram can tell us who is more active and important in the blogosphere clearly and intuitively. At that time, I knew the power of social network analysis. And after taking lecture 7, 8 and 9, I learned some basic conceptions about social network analysis and knew it better.
 

 
 
Social network analysis is the methodical analysis of social networks. It views social relationships in terms of network theory, consisting of nodes (representing individual actors within the network) and ties (which represent relationships between the individuals, such as friendship, kinship, organizational position, sexual relationships, etc.) These networks are often depicted in a social network diagram, where nodes are represented as points and ties are represented as lines. Social network analysis focuses on the social actors and the relationship between actors, and can be defined formally as a sociomatrix.
 
Here we got our first important distribution, Density, which reflects the proportion of direct ties in a network relative to the total number possible. And the other one is Centrality, which refers to a group of metrics that aim to quantify the "importance" or "influence" (in a variety of senses) of a particular node (or group) within a network. There are three important methods of measuring centrality.
 

• Degree centrality

Degree centrality is defined as the number of links incident upon a node. In the case of a directed network, we usually define two separate measures of degree centrality, namely Indegree and Outdegree. Accordingly, Indegree is a count of the number of ties directed to the node and Outdegree is the number of ties that the node directs to others. When ties are associated to some positive aspects such as friendship or collaboration, Indegree is often interpreted as a form of popularity, and Outdegree as gregariousness.

• Closeness centrality

Closeness can be regarded as a measure of how fast it will take to spread information from a particular node to all other nodes sequentially. And the more central a node is the lower its total distance to all other nodes. Closeness centrality describes the average distances between one node and all other nodes connected with it.

• Betweenness centrality

Betweenness centrality quantifies the number of times a node acts as a bridge along the shortest path between two other nodes. It was introduced as a measure for quantifying the control of a human on the communication between other humans in a social network by Linton Freeman.[9] In his conception, vertices that have a high probability to occur on a randomly chosen shortest path between two randomly chosen vertices have a high betweenness.
 
We can solve many problems by using social network analysis. Such as the sociogram of our blogosphere can reflect the condition of students’ interactions. The teacher can know which student’s blog is the most popular and students can know who does best too. What’s more, SNA even can be used to understand the diffusion of microfinance in the rural parts of southern Karnataka, an Indian state. The social network used was a union of many separate networks each of which captured a certain kind of interaction between villagers (e.g. people who go to the temple together, people who borrow from each other, etc.). And if you are interested in ‘How Social Network Analysis Solves Real World Problems’, I will recommend an article to you which you can read through http://www.digitaltonto.com/2011/how-social-network-analysis-solves-real-world-problems/. I hope it will help you.
 
Visualization and Measurement are the iron supports for the establishment of social network analysis. And the data used in the analysis should be achieved by empirical methods in the field of the social sciences. We can realize visualization by using SNA software such as Netdraw, and Ucinet for the realization of measurable.

  

https://sites.google.com/site/netdrawsoftware/

 

 

https://sites.google.com/site/ucinetsoftware/home

 
All in all, in this era of information explosion, effective social network analysis approaches play an indispensable and crucial role. It helps us make relationships clear between people, and make the right set of decisions easier. We should try our best to push the development of SNA since we benefit a lot from it.

Something about Cloud Computing & Answers for lecture 6.

Part 1. Cloud Computing

First, we come to talk something about cloud computing.

Nowadays, most computers are not stand-alone devices for personal computing, but connected together by Internet and used for both communication and individual computing activities. Cloud computing is not only a combination of computational capabilities. So, what is cloud computing? Cloud Computing is Internet-based computing, whereby shared resources, software, and information are provided to computers and other devices on demand. It also implies the services provided over the Internet as both the hardware and system software on the Data-centres provided by service provider.

Cloud computing comprises following two levels:
Front end users: customers or users using those service on their device
Back end users: service provider or vendor managing the services

Then, what’s the strong point of cloud computing? Let’s raise a common case, suppose that your mobile phone hasn’t been installed any app which supports PPT file. Thus any PPT document cannot be read on your mobile. But if you have Internet access, then, you can upload the file to your Google Doc account. And then, you can read the data of that PPT file on Google Doc. Thus cloud computing saves your time and money. You need not have any supporting software to run any application. The service provider provides the configuration required to run any application on your system. Moreover you also need not have high processing hardware. You only need a device (PC/MOBILE/PAD etc.) and web browser to access a cloud and run your application.

There are many types of public cloud computing:

• Infrastructure as a service (IaaS)
• Platform as a service (PaaS)
• Software as a service (SaaS)
• Storage as a service (STaaS)
• Security as a service (SECaaS)
• Data as a service (DaaS)
• Test environment as a service (TEaaS)
• Desktop as a service (DaaS)
• API as a service (APIaaS)
• Backend as a service (Baas)

And the three most important types are SaaS, PaaS and IaaS.

Software as a Service:
This is the most widely used Cloud Computing approach to date. Software as a service, delivers a single application through the browser to thousands of customers using a multitenant architecture. Like: you can edit a photo online on picself.cn so you need not install the photo editing software on your system- thus providing software as a service.
Examples: Gmail, Google docs, Skydrive.

Platform as a Service:
PaaS delivers development environments as a service. You can build your own applications that run on the provider's infrastructure and are delivered to your users via the Internet from the provider's servers. All the development tools will be provided to you.
Examples: Google App Engine, Windows Azure.

Infrastructure as a Service:
In the IaaS model, it provides you the infrastructure (like storage) required to host the services yourself i.e. makes you the system administrator and manage hardware/storage, network and computing resources. It is very much the backbone of the entire Cloud Computing concept.
Examples: Google Gears, Amazon Simple Storage Service.

There are four deployment models of cloud computing.

At last, I want to recommend you a website, IFTTT (If This Then That), www.ifttt.com. I think it’s kind of cloud computing website which consists of both SaaS and IaaS. You’d better have a try, and I believe you will like it.

Part 2. Answers for lecture 6.

Question 1.What is the definition of Social Cloud?
Answer: A Social Cloud is a resource and service sharing framework utilizing relationships established between members of a social network. It can change the situation that some individual users of a social network are bounded by finite capacity and limited capabilities. A cloud-based usage model is used to enable virtualized resource sharing through service-based interfaces.

Questions 2.What are the possible applications of a Social Cloud?
Answer: There may be five possible types of applications of a Social Cloud.
1. A Social Computation Cloud: such as web-based email service.
2. A Social Storage Cloud: such as SkyDrive by Microsoft.
3. A Social Collaborative Cloud: such as MyExperiment.org.
4. A Social Cloud for Public Science: such as BOINC.
5. An Enterprise Social Cloud: such as Moodle@CUHK.

In Activity One, my aim is to make an understanding of Social Could by reading the paper. But in Activity Two, my aim is to correct and update my previous answers by sharing ideas with my teammates, because I can refer to others’ understandings.

And I think there are some differences between individual and group epistemic cognition. Such as in terms of group epistemic cognition; the certainty, sources and justification of knowledge is stronger than individual cognition. We can learn from each other, not only from the article.

In Activity One, I answered the questions individually; just refer to the paper itself and my own experiences. But in Activity Two, I combine others’ ideas with my own and update my previous answers. I learned more through the process of interacting with my teammates.

Further study of Cognition and SCT

I access social networks nearly everyday, but I haven’t aware that social psychology is inside it before. After taking lecture 3&4, I know social networking more theoretical and in-depth. To some extent, social networking brings old-days’ face-to-face social psychology to the Internet. Which enable people from different region, stratum and background can interact with each other with no block. So I think that the social psychology in the Internet Age is more complicated and diversified.

An interesting point taught in the lecture is the hierarchy of cognition. A three-level model of cognitive processing to account for complex monitoring when individuals are faced with ill-structured problems, i.e., problems on which opposing or contradictory evidence and opinion exists, is proposed. Each level provides a foundation for the next one but is not subsumed by it.

• At the first level of cognition, individuals enter into cognitive tasks such as computing, memorizing, reading, etc. These are the pre-monitored cognitive processes on which the knowledge of the world is built.

        I think, in this level, we mostly focus on data collection and lay the foundation.

• The second level, metacognition, is defined as the processes which are invoked to monitor cognitive progress when an individual is engaged in level 1 cognitive tasks or goals such as those listed above.

        In this level, we can get information by data processing, checking on the cognition progress, evaluation of progress and so on. It can enhance the result of level 1.

• The third level, epistemic cognition is characterized as the processes an individual invoke to monitor the epistemic nature of problems and the truth-value of alternative solutions. It includes the individual’s knowledge about the limits of knowing (e.g., some things can be known and others can not), the certainty of knowing (e.g., some things can only be known probabilistically), and the criteria for knowing (e.g., one knows the answer to a question if it can be conclusively verified scientifically). It also includes the strategies used to identify and choose between the form of solution required for different problem types.

        In this level, the highest level of building knowledge, we share our own ideas with each other and rise above them to reach the goal of knowledge advancement, we learn from each other and discuss the issue with a goal to produce new knowledge through this learning process.

We can find a simple example of the model talked above from Douban Movie as followed.

Level 1: Cognition, (Data)

We can find some basic data of a movie at http://movie.douban.com/subject/3606971/.

Level 2: Metacognition, (Information)

After we have finished watching the movie, we can add it to our favorite list, and add tags to the movie.

Level 3: Epistemic Cognition, (Knowledge)

We can then, write a review of the movie, share our ideas, and discuss with each other.

This kind of example helps me learn the theories better, as I haven’t learnt psychology before.

We are not alone, and we’re all in the society, so, of course, we have to study Social Cognition. Social cognition is the encoding, storage, retrieval, and processing, of information in the brain, which relates to conspecifics (members of the same species). Also, Social cognitive theory, posits that portions of an individual's knowledge acquisition can be directly related to observing others within the context of social interactions, experiences, and outside media influences. In other words, people do not learn new behaviors solely by trying them and either succeeding or failing, but rather, the survival of humanity is dependent upon the replication of the actions of others. Depending on whether people are rewarded or punished for their behavior and the outcome of the behavior, that behavior may be modeled. Further, media provide models for a vast array of people in many different environmental settings. Such as the results of the famous Bandura’s Bobo Doll Experiment showed to us. Therefore, we should use the social networking properly and set right models.

After taking Lecture 3&4, I know that the base of social networking is not computing or codes, but psychology; it’s just a show perform at another stage.

REFERENCES

Karen Strohm Kitchner, Cognition, Metacognition, and Epistemic Cognition: A Three-Level Model of Cognitive Processing, S. Karger AG, Basel, 1983

Social Cognition: http://en.wikipedia.org/wiki/Social_cognition

Social Cognitive Theory: http://en.wikipedia.org/wiki/Social_cognitive_theory

An interesting beginning of learning Social Networking

Several years ago, when I began to use XiaoNei (the precursor of RenRen), Youku and Weibo, I hadn't realized that these websites are social networking websites. Until the movie The Social Network (2010) released (http://www.imdb.com/title/tt1285016/), I realized the simple conception of Social Network for the first time, but I didn't know the detail. After learning the first two lectures, I have gained some basic knowledge of social networking. Such as some definitions as followed. 

• Social Networking Service: an online service, platform, or site that focuses on facilitating the building of social networks or social relations among people who, for example, share interests, activities, backgrounds, or real-life connections.
• Social Media: a group of Internet-based applications that build on the ideological multi faceted and technological foundations of Web 2.0, and that allow the creation and exchange of user-generated content.
• Social Computing: a general term for an area of computer science that is concerned with the intersection of social behavior and computational systems.
• Social Task: a larger view of collaborating work in a social network.
• Social Media Marketing: center on efforts to create content that attracts attention and encourages readers to share it with their social networks.

When we come to the case study of PowerReview, I found that Social Recommendations are very interesting. There are two types of Social Recommendations. The first one is Direct Social Recommendations. Which is simple and directly. And the second one is Derived Social Recommendations, which is more complicated and intelligent. Here I focus on the Derived Social Recommendations, and find some information after class.

 

Collaborative filtering methods are based on collecting and analyzing a large amount of information on users’ behaviors, activities or preferences and predicting what users will like based on their similarity to other users. There are two types of collaborative filtering, first one is User-based filtering, also named Memory-based filtering, the results of user-based filtering depends on the people who share similar taste with you. If they like the item, then, maybe you prefer it too. For example, if user A like item a, user B like item a, b and c, user C like a and c, then we think user A is similar to user B and C because all of them like item a. Because user B and C like item c too, so the system will then recommend item c to user A. The second one is Item-based filtering, also called Model-based filtering. The results of item-based filtering depend on your previous preferred items. For example, if user A like item a, and item b is similar to a, then, the system will commend item b to user A.

I think the biggest difference between these two filtering methods is that item-based filtering depends on the user’s own taste while user-based filtering depends on others'. In my opinion, the results of item-based filtering maybe more accurate than user-based filtering, because the parallel among items is more stable than users. The chance of different users liking same items is relatively low.

Here we come to Content-based filtering. It’s a method that based on information about and characteristics of the items that are going to be recommended. Whether an item will be recommended to the user depended on the scores calculated according to these preferences and characteristics.

Maybe we could combine the content-based filtering with collaborative filtering in order to get improved recommendations. So, if you are interesting in this aspect, plz leave a comment, so we can have a discussion and exchange ideas.

It's interesting to study the theory of Social Networking while experiencing the relevant famous websites. I will make a list of these websites later.

Welcome to comment on my blog, and we can make progress together.

REFERENCES

• Social Networking Service  http://en.wikipedia.org/wiki/Social_Networking
• Social Computing  http://en.wikipedia.org/wiki/Social_computing
• Social Media Marketing  http://en.wikipedia.org/wiki/Social_media_marketing
• Badrul Sarwar , George Karypis , Joseph Konstan , John Reidl, Item-based collaborative filtering recommendation algorithms, Proceedings of the 10th international conference on World Wide Web, p.285-295, May 01-05, 2001, Hong Kong, Hong Kong