Scholarly article on topic 'Towards Identifying the Challenges Associated with Emerging Large Scale Social Networks'

Towards Identifying the Challenges Associated with Emerging Large Scale Social Networks Academic research paper on "Computer and information sciences"

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Procedia Computer Science
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{"Social Networks" / Privacy / "Data Analysis"}

Abstract of research paper on Computer and information sciences, author of scientific article — Haroon Malik, Ahsan Samad Malik

Abstract Social networking sites like Facebook, YouTube, Orkut and twitter are among the most popular sites on the internet. Users of these sites form a social network (SN), which provides a powerful mean of sharing, organizing, and finding contents and contacts. However, the rate at which SNs are growing, posses many latent challenges in maintaining the stability of their underlying systems and the members associated with them. We discus some of the current challenges associated with these emerging SNs; In particular the challenge in the analyzing of large scale SN data, challenge in developing the tools and techniques required to extend these large scale SNs, and privacy issue related to SNs.

Academic research paper on topic "Towards Identifying the Challenges Associated with Emerging Large Scale Social Networks"

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Procedía Computer Science 5 (2011) 458-465

The 2nd International Conference on Ambient Systems, Networks and Technologies

Towards Identifying the Challenges Associated with Emerging

Large Scale Social Networks

Haroon Malika, Ahsan Samad Malikb,a*

aSchool of Computing, Queen's Univeristy, Kingston, Canada bGhulam Ishaq Khan Institute of Enginering and Technology, Pakistan


Social networking sites like Facebook, YouTube, Orkut and twitter are among the most popular sites on the internet. Users of these sites form a social network (SN), which provides a powerful mean of sharing, organizing, and finding contents and contacts. However, the rate at which SNs are growing, posses many latent challenges in maintaining the stability of their underlying systems and the members associated with them. We discus some of the current challenges associated with these emerging SNs; In particular the challenge in the analyzing of large scale SN data, challenge in developing the tools and techniques required to extend these large scale SNs, and privacy issue related to SNs.

Keywords: Social Networks; Privacy; Data Analysis

1. Introduction

A social network (SN) is generally defined as a system with a set of social actors (nodes) and a collection of social relations that specify how these actors are relationally tied together [1]. Social network analysis (SNA) is the study of relations between individuals, including the analysis of social structures, social positions, role analysis and many other [2]. Normally, the relationship between individuals, e.g., kinship, friends, neighbor's etc. are presented as a network.

Recently, the proliferation of rich social media, on-line communities, and collectively produced knowledge resources such as instant messaging (e.g., IRC, AIM, MSN, Jabber, Skype), sharing sites (e.g., Flickr, Picassa, YouTube, Plaxo), blogs (e.g., Blogger, WordPress, LiveJournal), wikis (e.g.,Wikipedia, PBWiki), microblogs (e.g., Twitter, Jaiku), web- based social networks (e.g., MySpace, Facebook, Ning), collaboration networks (e.g., DBLP) to mention a few, has accelerated the convergence of technological and social networks, producing environments that reflect both the architecture of the underlying information systems and the social structure on their members.

There is little doubt that the social networks have has briskly matured to the point where they are a vital part of daily life. As we become more reliant on online social networking sites to connect with our friends, peers and

* Corresponding author.

E-mail address:

1877-0509 © 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Prof. Elhadi Shakshuki and Prof. Muhammad Younas. doi:10.1016/j.procs.2011.07.059

business associates, we sometimes lose site of the fact we are generating an enormous amount of data about ourselves, potentially providing a treasure trove of demographics, opinions and behavioral information for third-party entities. With such huge amount of data dumped into SN, one of the most important challenges related to the SNA is dealing with such large volume of data (e.g., operating and tweets logs) that can be of terabytes in size. For such a large volume of data, it is almost impossible for humans to directly distinguish useful information patterns from the raw SN data. Most SNA tools have progressed little beyond data collection and presentation [30] [31]. Advances in user interfaces, threshold exceptions, real-time visualizations, and ad hoc reporting (logging/tweeting/blogging etc.) are evolutionary steps to SNs. However, these advances still require eye-balling the plots/graphs generated by various visualizations to uncover the pattern(s) of interest. SNA tools, with some degree of knowledge of the underlying structure of the target system and machine learning capabilities are required not only to infer social process from data such as, processes in on-line social systems related to communication [3][4], community formation[5][5][7][8], indeed synthesizing macro level models as degree distributions, diameter, clustering coefficient, communities, small world effect, preferential attachment, etc [19][20][21][22] from these social processes.

Most of the existing research is focused on the inference of social processes. Little effort had been dedicated in directions of developing business Intelligence techniques to quantify SN data for the purpose helping and boosting community lives (such as from law enforcement agencies striving to track criminal movements to advertising companies looking for insight into consumer behavior) due to latent challenges associated with the analysis of SN data. Examples of such effort spans over the areas of information seeking and collective problem solving[9][10][11], marketing[11][13][14][15], the spread of news[16][17], and the dynamics of popularity[18]. We provide a snapshot these challenges.

2. Challenges Associated with Large Scale Social Network Analysis

Unprecedented level of growth of SNs as evident form Table 1 and Table 2, resulting into the availability of massive volumes of data in an online setting has given a new impetus towards user-centric mining; A step toward harnessing the enormous amount of intelligence and context aware knowledge which lies in the head of general populace but is cultivated at social computing platforms. Such knowledge is not only important for creating future pervasive and ubiquitous environments, indeed it is required to fabricate strong modern societies. However, there lies many challenges associated with these emerging large scaled SNs — from managing the SN to its content analysis. Time is ripe to identify the challenges so that can be address in timely manner.

2.1. Large volume of data

SNs are living networks that daily give birth to data traces which can be up to exabytes in volume. For example, Facebook produce more than a petabyte of data per day. Even it's logging data excess 25 terabytes per-day [24]. My spaces produce more than a petabyte of social data per-day[23].According to Google, it create as much information ( social blogs and orkut )in two days now, as we did from the dawn of man through 2003 i.e., one exabyte of data[26]. Analysts need to analyze this huge plethora of SN data to support system management activities in limited time i. e, to see system overload, trends and patterns to tune the SN software and to launch new SN features and product in timely manner to help social communities interact robustly.

Sadly, the processing of SN data in timely manner is still a big challenge [25]. Implementation and analysis of for processing the huge cloud of social data based on super computer such as Cray [27][28] used by SN analyst is not a feasible solution. Other methods used by social network companies such as Facebook, Twitter and LinkedIn that can scale up to thousands of commodity machines to provide the same power as super computers are still in experimental phase [29]. MapReduce model developed by Google, specifically for processing SN data is de facto for now [30].

However, as the data changes rapidly and constantly, there is need for improved and more efficient methods. The expertise from researcher have been dedicated in establishing frameworks for mining the social network data as seen in Fig 1 for the prospective of third party user that can be useful for various domains such as marketing, Crime preventions etc, but not to the practitioner. They are still hanging with the traditional data mining techniques.

Table 1: Numbers of members (accounts) for web SNS as of 2010.

No. Website Members No. Website Members

1 Tickle 18,000,000 10 Amigos 3,500,000

2 Friendster 90,000,000 11 Orkut 100,000,000

3 Friend Finder 15,700,000 12 Twitter 75,000,000

4 Black Planet 14,000,000 13 2,600,000

5 Hi5 80,000,000 14 LinkedIn 80,000,000

6 Qzone 200,000,000 15 Zero Degrees 1,300,000

7 My Space 130,000,000 16 Out Personals 1,050,000

8 Live Journal 5,7000,000 17 Facebook 500,000,000

9 Friend Finder 3,600,000 18 Flicker 32,000,000

Table 2: Number of members per SN data source (DS)

Purpose /DS Sites Members

Blogging 5 5,700,000

Business 16 13,300,000

Dating 23 49,000,000

Pets 6 80,000

Photos 7 1,1015,000

Religious 11 1,650,000

Entertainment 55 900,000,000

Fig 1 Steps involved in mining SNs data.


Search > Scratch > Recover Analyze


2.2. Unstructured and inconstant data

There is also a shift towards non-text contents in the form of sound, music, pictures and video that renders basic data mining techniques ineffective in recovering content that can be interpreted and collated by means of automated software. Attempt to recover and interpret such content is technically challenging and fraught with legal danger from illegal content of various types. There are some ad-hoc scripts developed by researchers to facilitate the "recover" phase of SN mining as shown in Fig 1. lHowever, these scripts are written to satisfy personal and directive research interest for particular SN and cannot be generalized. A remaining challenge to test the recovered content and its interpretation is essentially semantic in nature. Given the level of slang, abbreviations, and assumed knowledge on the part of social networking sites participant, interpreting recovered conversations and content can be quite difficult. Such intelligent interpretation and recover of profile content can lead to crime prevention or detection and greater public safety.

2.3. Validation challenge

One of the speed bump the development of effective algorithms and techniques for improving the overall experience of both social users and SN researchers is the lack of access to real SN system for the validation of results. For example, techniques have been proposed improve search in SN [1], mitigate email spam in SN [32], and to defend against Sybil attacks [33]. However, these techniques have not yet been evaluated on real SN at scale known today. On other side, without proof of proper validation, practitioner hesitates to apply these techniques to SN as they don't want to take any risk in violation of service level agreements (SLAs) or disruption in performance; which can result in large monetary losses.

2.4. Lack of simulation tools

As the theories and new ideas for emerging large scale social network are frequently published based on sophisticated mathematical models, there is no framework to test their ground realities. For example, large amount of social network research lies on assumptions made by Erdos [34] regarding topology and distance in random graphs analogous to small-world network topologies [35]. None of them has emerged as a clear winner as there is no means to test these theories. A simulation tool that is proven and validated through docking and comparison with empirical results can be used as means to test validity of multiple theories. Therefore, an important challenge that

lies on the road to SN development is to build robust simulation tools.

More importantly, as number and complexity of social network analysis algorithms grows; it becomes more and more important to test the algorithm for accuracy, scalability and robustness. Testing of the algorithm and any piece of software associated with it on machine-generated data using simulation, as opposed to empirical data only, allows the SN practitioners and researcher to conduct repeatable test. The repeatable test stresses certain aspect of SN software/system and help in debugging and optimization of its performance.

2.5. Lack of visualization support

Data visualization makes the SN data better understandable and can reveal unknown details such as communities or clusters in a network, but also describes the overall structure. Visualization of social networks is challenging because of the visual pollution generated by large number of colors, overlapping connections and space crunch. Especially in social networks, where the nodes are densely interconnected with diverse node strength, the prevalent visualization methods of social network diagrams are less than effective to read the data and derive meaningful patterns.

Tools such as Graphviz, Open Calais, Maltego, touch graph, mind raider and Vizster have been used to visualize SN data by researchers however, these tools were not designed specifically for SN hence suffer scalability issues[7].

2.6. Privacy challenges

Recent years have seen unprecedented growth in the application of SNs, with about 300 SN systems collecting information on more than half a billion registered users [38]. As a result, SNs store a huge amount of possibly sensitive and private information on users and their interactions. This information is usually private and intended for the eyes of a specific audience only. However, the popularity of SNs attracts not only faithful users but parties with rather adverse interests as well [39][40]. The diversification and sophistication of purposes and usage patterns of SNs inevitably introduce privacy infringement risks to all SN users as a result of information exchange and sharing on the Internet. It is therefore not surprising that stories about privacy breaches by Facebook and MySpace appear repeatedly in mainstream media [39][41]. Regardless of the type of SN, user has to join it by disclosing some of his personal information to create his/her profile. For example, as shown in Table 2, three hundred million users had to disclose their personal information to join in social entertainment.

Disclosing personal information is SNs is a double-edged sword [42]. On one hand, information exposure is usually a plus, even a must, if people want participate in social communities. Visibility of user's profile and public display of connections (fried lists) are necessary for implementing core functionalities of SN, such as social search and social traversal. On the other hand, leakage of personal information especially one's identity, may invite malicious attacks form real world and cyberspace, such as stalking, reputation slander, personalized spamming, and phishing [43].

Despite the risk's many of the privacy and access control mechanism of today's SN are purposely weak to make joining the SN and sharing information easy. Hence, it is a major challenge to timely develop more effective and flexible security mechanism for the safety of SN users with minimal impact on the thriving of SNs.

2.7. Trust Construction Challenge.

Trust in social network is related to relationships with strangers. Usually in the internet, trust is related to problem in privacy and security. That is why the current solution for trust is related to privacy and security such as certificates and data transfer security [44]. There is a need to figure ways of applying real world behaviors in social networks such as building trust between users. There is also a need for a trust network where users can evaluate new relationships and have different levels for these relationships.

Due to inherent fuzziness associated with trust, it's not surprising that there exists no single definition for trust across all disciples and contexts where it has been studied. However, the trust in context of SN is closer to our everyday life i.e., we rely recommendation from others (whom we trust) to decide the best place to buy a desired product of avail a service. In such scenario, our social network becomes more effective as we promptly can rely on recommendations coming from our friends. The degree of trust we have in our recommender friend plays an

important role in ascertaining how much we would value his/her recommendation. Recommendation system such as forums and search engines has long been used on internet to answer user queries [45]. But even the best search engine and discussion forum do not give us personalized responses to queries and we end up getting inputs from people who often have disparate taste. People prefer recommendation from their friends [46] rather than general recommendation systems. In particular users prefer recommendation from system they trust. Such evidence motivates the use of social network to drive a trust-based recommendation system.

However, trust-based recommendation system in context of SN is not easy to build as they require satisfying at least four characteristics pertaining to virtual communities in SN which are:

1. Multidimensionality: There are many factors to be considered to evaluate the trust. Usually we have to take many traits of the party into account, such as honesty, experience, precision, efficiency or cooperativeness. The broader social space may bring further dimensions.

2. Contextuality: Not only the social context does matter, but also the purpose of trust evaluation or the 'contextual theme'. We may talk about theme-contextual trust, such as when you are in search for reliable advices and you trust more to experts on certain domain.

3. Scope of relevance: Trustor performs his trust evaluation within a virtual community. The result reflects his subjective view. Besides this, one may talk about community-wide or system-wide metrics, referred to as reputation or trustworthiness.

4. Lack of soft indices: In a virtual space one actually miss many relevant non-verbal indices which usually help us in the process of trust formation. One do not see the person in real, sometimes one even do not see him at all. It is also more likely that there are no other trustful people around who could share their opinions based on their direct personal experiences.

It is not easy to build a trust model based on the above four characteristics due to following reasons: Firstly, one is faced with the subjective problem of quantifying interpersonal trust between users. Secondly, a sound estimation of transitive trust between two users who are not directly connected in the social network is imperative to build a successful recommendation system. Lastly, one has to devise an efficient (on bandwidth usage) yet effective query propagation mechanism in which the queries may be selectively routed to users who have sufficient knowledge to respond to them. These difficulties become manifold when we want to design the recommendation system in a distributed setting and have it work even if some of the nodes of the network are down. On top, there is more effort being pushed on how to deceive the trust rather to quantify and built it for SN. This includes:

1. Scamming and phishing: Scammers are increasingly more proficient, with both technical and social skills. One would probably never give money based on poorly written scam e-mail. But what if one is contacted by his/her friend or relative, who has been coincidentally trapped somewhere without a coin?

2. Impersonating and profile hijacking: One of trends is creating false profiles or hijacking profiles for scamming or similar fraudulent purposes. Ironically, the illusion of security on sites which take safety seriously may lower cautiousness of users, leading to even higher dangers if the fraud occurs. [47]

3. Cyberstalking: Social networks give vital ground for cyberstalking or cyberbullying, varying from false accusations to gathering information for further harassment.

4. Trust authority compromising: Institutional trust authorities are targeted often by attackers and they are vulnerable. Besides this, power-law distribution where rich becomes even richer works in trust systems similarly - trusted nodes tend to receive even more trust. It leads to constitution of so called 'trust hubs' [48], informal trust authorities in a space of the social network. Importance of institutional or informal trust authority intensifies the impact when the authority is being compromised.

Caverlee et al. [49] noted two aspects of current social networks, which make the dangers even worse. First, the small world phenomenon causes, that there is a short distance in the network between any two participants. Even if user is able to control his direct friends, malicious users may be only few hops further. Second, the user has limited network view, so even if he controls his friends and maybe friends of friends, he has no idea about credibility of other participants. More work is needed in areas such as:

1. Complicated settings: Algorithms and metrics to manage trust information shared e.g. in multi-

agent systems [50] and under conditions of uncertainty.

2. Continuous fight with deceivers: Continuously search for ways how to keep networks useful and

sufficiently safe and trusty, despite increasing activities of defective peers or agents with random, selfish or even malicious behavior It's a never-ending fight with strikes and counter-strikes.

3. Privacy: Keep privacy questions on mind while dealing with the security. Sometimes the

requirements may be contradictory.

4. Trust identity: It will be furthermore a long path from system-wide trustworthiness to a global

trust identity, shared among systems. Ontologies seem to be a good glue to facilitate the interoperability, but yet have not truly evolved.

There lies a challenge to secure the social software itself, foster growth of confidence among users and their content and deal with all those matters of trust mentioned in the systems with steadily increasing dynamics, where millions of users are joining, performing their activities and leaving.

3. Conclusion

Social networks produce an enormous quantity of data [19][24][23][25][26]. There are many challenges associated with analyzing this vast quantity of unstructured and in many case inconsistent data. We have identified and provided a snapshot of the challenges associates with the state of art. This snapshot will act as foundation in driving researcher to address the SN challenges thereby facilitating both i.e., practitioners in managing SN and SN user to enhance their social experience.


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