SNA in the Age of Big Data

The Age of Big Data

“It’s a revolution. We’re really just getting under way. But the march of quantification, made possible by enormous new sources of data, will sweep through academia, business and government. There is no area that is going to be untouched.”
-- says Gary King, director of Harvard’s Institute for Quantitative Social Science. [1]

"They are our nuclear codes."
-- Ben LaBolt, the campaign spokesman responded so when mentioned about the data analysis help Obama win. [2]

Welcome to the Age of Big Data. Data flows everywhere in the contemporary era, and it is growing rapidly all the time. IDC, a technology research firm, estimates that the data is growing at 50 percent a year, or more than doubling every two years. It’s not just more streams of data, but entirely new ones. For example, there are now countless digital sensors worldwide in industrial equipment, automobiles, electrical meters and shipping crates. They can measure and communicate location, movement, vibration, temperature, humidity, even chemical changes in the air. [1] 

Data analysis has been widely applied in various industries, business, science, sports, advertising and public health, almost every area we could imagine. Data-driven discovery and decision-making are playing an increasingly important role in our life. Recently, An article in the Time briefly describes how Obama beat Romney by the data collected, stored and analyzed. 

Social Network Analysis

Despite various approaches for data analysis have been introduced and applied, most of them assume that what people do, think, and feel is independent of who they know. This obviously brought bias on result when the analysis relates to human behavior. In this case, Social Network Analysis, which based on the assumption that  people are all interdependent, could show its great power.

 SNA has gained significant attention in recent years, largely due to the success of social networking and media sites, and the consequent availability of a huge mass of social network data. As the concepts and major algorithms of SNA have been introduced in our lecture notes, I will not repeat them here. 

Here I'd like to borrow an example from Inside Social Network Analysis:

SNA applies to a wide range of business problems, including: [3]

• Knowledge Management and Collaboration.  SNAs can help locate expertise, seed new communities of practice, develop cross-functional knowledge-sharing, and improve strategic decision-making across leadership teams. 
• Team-building.  SNAs can contribute to the creation of innovative teams and facilitate post-merger integration.  For example, SNAs can reveal which individuals are most likely to be exposed to new ideas. 
• Human Resources.  SNAs can identify and monitor the effects of workforce diversity, on-boarding and retention, and leadership development.  For instance, an SNA can reveal whether or not mentors are creating relationships between mentees and other employees.   
• Sales and Marketing.  SNAs can help track the adoption of new products, technologies, and ideas.  They can also suggest communication strategies. 
• Strategy.  SNAs can support industry ecosystem analysis as well as partnerships and alliances.  They can pinpoint which firms are linked to critical industry players and which are not. 

Another paper, Social Network Analysis and Mining for Business Applications also provides detail about the business application of SNA, including more applications and challenges for SNA.[4] 

Most recently since the IPO of Facebook, it seems that the investments on SNS have been cooling down. An obvious factor is the revenue models for most SNS are ambiguous. However, I believe that with more studies and applications on social network mining and analysis, situation will go much better. (One typical case is Sina Weibo) 



Reference
1. STEVE LOHR, The Age of Big Data, February 11, 2012, The New York Times.
2. Michael Scherer, Inside the Secret World of the Data Crunchers Who Helped Obama Win, Nov. 07, 2012
3. Kate Ehrlich & Inga Carboni, Inside Social Network Analysis.
4. Bonchi, F., Castillo, C., Gionis, A., and Jaimes, A. 2011. Social Network Analysis and Mining for Business Applications

Picture Source: Greenbookblog

Revised on 28 Nov 2012

Social Circles Online and Offline

In lecture 7, Prof. Chan introduced some fundamental concepts about social network analysis such as dyad and triad. As we know, digital network is a mapping and extension to social relationship in our real life.

We define a group share same experience,(say, have same hobby or situation) as a social circle. Social circles in real life could be divided into four types below:[1]

Dyad

Type 1: One to one, or point to point. This is a circle contains two persons only, just same as the dyad mentioned in the lecture. The point to point relationship is an ultra-steady state. It is our core social state and has most frequency degree.

Type 2: Point to specific points, which is a steady state. One member communicates with all members in the circle. The circle could last long, but has less importance and frequency degree than one to one.

Type 3: Communication among members in a big circle/community, could be regarded as point to unspecific points, an infra-steady state. It may transfer to ultra-steady or steady state circles, but social behaviors tend to reduce in this circle as time goes by. For instance, colleagues in same company/department, cyber-pals in a game/BBS, classmates in certain program/course (IE QQ group) could be regarded as the big circle, and any member could communicate with other members. For some certain periods, especially for the initial period we join the circle, we would like to communicate with all or most members. Once we build friendship with one or some members in the circle, we will communicate more in the new point to point (or point to specific points) circle and reduce social behaviors in the big circle. Another possible case is that a member fail to find any member I'm interested after a certain period, then he/she will also reduce communications in the circle. Generally speaking, if this kind circle has no new members, it will become inactive eventually.

Type 4: Communication among members in a huge circle/community without boundary. This could be called as point to all points, which is an unsteady state.It is temporary, discrete and random. The relationship ends as soon as the communication is over until next communication starts. In our daily life, this not going to happen, we cannot broadcast to everybody in most cases. However, this could happen online.

Each person has many circles. We share and acknowledge different information in different circles.

As I mentioned, digital network is a mapping and extension to our real social environment. Now, let's take a look at what roles current SNS products are playing on.

Relationships on Facebook is more likely type 3 mentioned above. In most cases, we share pictures and statuses to all friends. It is not that stable.But the wall and message function enables point to point communication to satisfy users' need on type 1.

Twitter/Weibo looks more likely type 4.But due to the "follow" mechanism, in most cases, we know who follow us and our twits will be read by whom (not the case for public account). Hence we could also consider it as type 3. Sina Weibo seems try to map type 2 by the "close friend（密友）" function in latest V5 version, but it looks not very successful so far.

Other knowledge-based online communities such as Wikipedia, could be considered as type 4. We could say there is little social relationship on Wikipedia.It seems difficult to build relationship in these knowledge sharing communities.

IM and email is mostly used for point to point relationship.

It seems that no SNS could map all relation types circles at same time. In this regard, WeChat may be a good try, although it is not good enough.

Somebody says it is vain to map our complicate real life to online world. What do you think?

------------Supplement-----------
Added in 19 Nov 2012:
I found that the description may be ambiguous for type two circle in the article, so add an example here: suppose there are four persons A, B, C and D familiar to each other, they often gather together, we could say this is a type two circle. Generally speaking, one person has one or two such circles. And this kind circle usually contains only a few people.

In our real life, if a circle contains more people, the circle tends to less stable, as the cost to maintain it is higher.

Reference:
1.普通人的关系缺乏“全部公开”和“点对点”之外的中间状态吗？

Individual and Group Cognition about Social Cloud

Picture source: Cutcaster

In the latest lecture, two questions are raised for the article Social Cloud Computing: A Vision for Socially Motivated Resource Sharing:

1. What is the definition of Social Cloud?

2. What are the possible applications of a Social Cloud?

The answers are easily found in the article:

1. A Social Cloud is a resource and service sharing framework utilizing relationships established 

between members of a social network.

2.  A Social Computation Cloud; A Social Storage Cloud; A Social Collaborative Cloud; A Social Cloud for Public Science; An Enterprise Social Cloud.

- Is there any differences in terms of individual and group epistemic cognition, how?

All of our group members got the same answer except for Guan Hao, who had more comments on the definition by searching the Internet. And we have all agreed to adopt the comments. The comments are below:

Social Cloud can provide some kinds of services and these services are actually provided and maintained by a social network instead of centralized servers. The type of the services does not matter, it can be computational work, storage, collaborative… therefore there are lots of applications listed in this article. As long as the services are provided by a social network and it utilizes the relationships established in a social network, it is considered as a Social Cloud.

- How did you approach to the problem individually and in group, respectively? Is there any differences in the processes involved?

As for the epistemic aim, I believe there is difference between two activities. For activity one, due to the time limit, I just tried to understand the article and the new concept, to answer the questions; while for activity two, we have already had answers about the social cloud, and our aims are more likely to verify whether the answers are correct and explore more about the concept.

The approach is also different. For activity one, I just try to find answer in the article. In the activity two, more approaches are added including discussing and searching the Internet. However, due to time limit, we didn't find much more about it.