Download Social Graph

The social graph is a graph that represents social relations between entities. In short, it is a model or representation of a social network, where the word graph has been taken from graph theory. The social graph has been referred to as "the global mapping of everybody and how they're related".[1]

The term was popularized at the Facebook F8 conference on May 24, 2007, when it was used to explain how the newly introduced Facebook Platform would take advantage of the relationships between individuals to offer a richer online experience.[4] The definition has been expanded to refer to a social graph of all Internet users.

Download File 🔥 https://byltly.com/2y4Cmq 🔥

Since explaining the concept of the social graph, Mark Zuckerberg, one of the founders of Facebook, has often touted Facebook's goal of offering the website's social graph to other websites so that a user's relationships can be put to use on websites outside Facebook's control.[5]

Several issues have come forward regarding the existing implementation of the social graph owned by Facebook. For example, currently, a social networking service is unaware of the relationships forged between individuals on a different service. This creates an online experience that is not seamless, and instead provides for a fragmented experience due to the lack of an openly available graph between services. In addition, existing services define relationships differently.

As of 2010[update], Facebook's social graph is the largest social network dataset in the world,[6] and it contains the largest number of defined relationships between the largest number of people among all websites because it is the most widely used social networking service in the world.[7] Concern has focused on the fact that Facebook's social graph is owned by the company and is not shared with other services, giving it a major advantage over other services and preventing its users from taking their graph with them to other services when they wish to do so, such as when a user is dissatisfied with Facebook. Google has attempted to offer a solution to this problem by creating the Social Graph API, released in January 2008,[8] which allows websites to draw publicly available information about a person to form a portable identity of the individual, in order to represent a user's online identity.[9] This did not, however, experience Google's desired uptake and was thus retired in 2012.[10] Facebook introduced its own Graph API at the 2010 f8 conference. Both companies monetise collected data sets through direct marketing and social commerce.[11] In December 2016, Microsoft acquired LinkedIn for $26.2 billion.[12]

Facebook's Graph API allows websites to draw information about more objects than simply people, including photos, events, and pages, and their relationships between each other. This expands the social graph concept to more than just relationships between individuals and instead applies it to virtual non-human objects between individuals, as well.[13]

We have kept the TAO API simple on purpose. For instance, it does not offer any operations for complex traversals or pattern matching on the graph. Executing such queries while responding to a user request is almost always a suboptimal design decision. TAO does not offer a server-side set intersection primitive. Instead we provide a client library function. The lack of clustering in the data set virtually guarantees that having the client orchestrate the intersection through a sequence of simple point and range queries on associations will require about the same amount of network bandwidth and processing power as doing such intersections entirely on the server side. The simplicity of TAO API helps product engineers find an optimal division of labor between application servers, data store servers, and the network connecting them.

The TAO service runs across a collection of server clusters geographically distributed and organized logically as a tree. Separate clusters are used for storing objects and associations persistently, and for caching them in RAM and Flash memory. This separation allows us to scale different types of clusters independently and to make efficient use of the server hardware.

There are two tiers of caching clusters in each geographical region. Clients talk to the first tier, called followers. If a cache miss occurs on the follower, the follower attempts to fill its cache from a second tier, called a leader. Leaders talk directly to a MySQL cluster in that region. All TAO writes go through followers to leaders. Caches are updated as the reply to a successful write propagates back down the chain of clusters. Leaders are responsible for maintaining cache consistency within a region. They also act as secondary caches, with an option to cache objects and associations in Flash. Last but not least, they provide an additional safety net to protect the persistent store during planned or unplanned outages.

Social networks often require the ability to perform low latency graph computations in the user request path. For example, at LinkedIn, we show the graph distance and common connections whenever we show a profile on the site. To do this, we have developed a distributed and partitioned graph system that scales to hundreds of millions of members and their connections and handles hundreds of thousands of queries per second. We published a paper in the HotCloud'13 Conference, June 2013 that describes one of the techniques we use to keep latencies low:

A key challenge in a distributed graph system is that, when the graph is partitioned across a cluster of machines, multiple remote calls must occur during graph traversal operations. At LinkedIn, more than half the graph queries are for calculating distance between a member and a list of member IDs up to three degrees. In general, anywhere you see a distance icon on the LinkedIn site, there's a graph distance query at the backend. So, scalability and latency for this call are major considerations.

NCS, the caching layer, calculates and stores a member's second-degree set. With this cache, graph distance queries originating from a member can be converted to set intersections, avoiding further remote calls. For example, if we have member X's second degree calculated, to decide whether member Y is three degree apart from member X, we can simply fetch Y's connections and intersect X's second degree cache with Y's first degree connections.

A member's second-degree set is built in real time on every visit. On average, this cache has a hit ratio of over 90% to serve graph traversal queries. When cache miss occurs, the network cache service builds the cache in real time by gathering members' second-degree connection information from the GraphDB cluster. So the long tail of the graph distance queries comes from second-degree network cache miss. We use a brute force algorithm to compute this cache. The member's second-degree connections are gathered from several GraphDB nodes that store them. Each GraphDB node performs carefully tuned parallel merges to construct the partial result before sending the data back. A single NCS node is responsible for merging all partial results into one final second-degree array.

We were able to modify this greedy algorithm by taking advantage of an additional property of our system: GraphDB nodes belonging to the same replica provide one copy of the entire graph, and there is no partition overlap among the nodes. We concluded that nodes from the same replica were more likely to provide greater coverage.

The strength of the connections and topicality can then be scored. Certainly the goal here seems to be for delivering advertising, but obviously it can also play into the social elements of personalized search as well.

The system can also include a social graph linking module configured to determine a social network graph for at least a portion of the social network from the information received by the interface, the graph including a plurality of nodes connected by links, each node corresponding to a user that is registered with the social network and that has a profile page on the social network. The system can further include a score seeding component that identifies first nodes from the plurality of nodes as including content associated with a particular subject of interest and that seeds the identified first nodes with first scores that indicate profile pages for the identified first nodes are positively identified as including content associated with the particular subject of interest.

Content can be detected on a social network with greater efficiency. Instead of relying upon manual review of the pages of a social network, pages that likely contain content can be quickly located based upon links between users of the social network. A greater amount of content can be located on a social network in less time than under traditional manual review. Additionally, detection of content on the social network using links between users permits for a high degree of accuracy. Furthermore, detecting content based on links between users of a social network can have greater accuracy and efficiency than other automated techniques, such as content-based detection techniques.

These can be derived from aspects of the user profile, or expressly provided by the user. And of course, given how limited their data was prior to social networking, we can see what a boon this kind of deeper data can mean to Google.

This was one of the early sets of patents from Google (filed in 2007) on social profiling and certainly a forerunner to these ones. Again, the ultimate goal in those was about targeting influencer types and advertising.

By not only looking at the content being shared (and re-shared) but the other factors above, Google can indeed make some interesting social connections and gain some detailed data on those using the network. Sure, they can (and do) also look at other networks, but that pales in comparison to the detailed data they can get from those on Google +. e24fc04721