294 results about "Social network analysis" patented technology

Enabling dynamic, computer-driven, context-based detection of social network patterns within an input graph representing a social network. A Social Network Aware Pattern Detection (SNAP) system and method utilizes a highly-scalable, computationally efficient integration of social network analysis (SNA) and graph pattern matching. Social network interaction data is provided as an input graph having nodes and edges. The graph illustrates the connections and/or interactions between people, objects, events, and activities, and matches the interactions to a context. A sample graph pattern of interest is identified and/or defined by the user of the application. With this sample graph pattern and the input graph, a computational analysis is completed to (1) determine when a match of the sample graph pattern is found, and more importantly, (2) assign a weight (or score) to the particular match, according to a pre-defined criteria or context.

Methods, apparatuses, and computer-readable media for generating a social network graph to model one or more social networks of related authors of online social media and their corresponding posts of online social media conversations relevant to subject matter of interest in a category. Embodiments are configured to harvest and aggregate posts of online social media conversations from one or more online social media sources; to perform content scraping on the posts of online social media conversations to obtain raw data that includes user-profile information of a social media author corresponding to each individual post of online social media conversations; and performing social network analysis processing on the raw data to obtain the social network graph.

A method, system, and apparatus are provided for creating a knowledge interchange (“KI”) profile for a user that identifies the interests of the user, the individuals with which the user has communicated, and the topics on which the communication with the user was made. In order to create the KI profile, items of interest are gathered at the client computer by monitoring the interaction between a user and programs executing on the client computer. Once the items of interest have been identified, the KI profile is created by performing a social network analysis, an interest analysis, and a noun phrase analysis. Once the KI profile has been created, it may be uploaded to a server computer where it is aggregated with KI profiles created for other users.

Traditional methods of prediction of the likelihood of churning in a given market such as wireless telecommunications are improved by inclusion of social network analysis to predict churning by members of a population in response to churning by any given individual member of that population. A sphere of influence of individual members of the population on other members of the population are modeled and analyzed through a plurality of degrees of separation and quantitatively evaluated by a number of relational algebraic joins in which at least one such join includes weights to assert a diminishing influence with number of degrees of separation.

A method, system, and computer program product for enabling dynamic detection of anomalies occurring within an input graph representing a social network. More specifically, the invention provides an automated computer simulation technique that implements the combination of Social Network Analysis (SNA) and statistical pattern classification for detecting abnormal social patterns or events through the expanded use of SNA Metrics. The simulation technique further updates the result sets generated, based on observed occurrences, to dynamically determine what constitutes abnormal behavior, within the overall context of observed patterns of behavior.

Method of analyzing data from an online social network having a set of network users. The method includes obtaining topic-specific messages sent by the network users. The messages have social content provided by the networks users, wherein the messages include a marker of interest in the social content. The marker of interest is associated with a topic of the social network. The method also includes identifying the network users that sent the messages having the marker of interest in the social content as interested users of the topic. The interested users are a subset of the set of network users. The method also includes determining a topic-specific influence (TSI) value of a designated user from the interested users in the subset. The TSI value of the designated user is based on a number of the interested users that are registered to receive the messages from the designated user.

This invention is generally directed to one or more systems or methods relating to social network analysis. More specifically, this invention is generally directed to one or more systems or methods relating to personal communication networks and the analysis of personal-communication-network data.

The invention provides a video recommendation system and a method thereof. The video recommendation system and the method thereof comprises: acquiring multi-source data including personal information, social network information and video classification information of users by a information acquisition module; presetting the multi-source data by a data pre-processing module; storing the multi-source data after preprocessing by a user database which is established by a data storage module; enabling an analytical module of the characteristics of users to acquire feature and mood information of the users according to microblog data sent by the users and enabling an analytical module of the social network information to analyze friend groups of the users to acquire friend circles; enabling a video recommendation module to select favorable videos of the users according to the feature, mood and friend circles; enabling a front-end display module to display the selected videos for the users.

A computer-implement method and the associated system of computing resources provides an automated work force management capability that optimizes work assignments for individual workers using work force management techniques in combination with social networking analysis (SNA). The work force management attributes are enhanced using SNA. Bipartite graphing processes are used to match the socially enhance worker attributes with the work requirements. By combining the social networking information with the work force management attributes, work assignments are optimized. This optimization exploits historical social interactions between workers and combines their influence with the skills and other work force attributes of each worker required for job performance.

The invention relates to a criminal suspect mining association method and a system based on social network analysis and is characterized in that the method comprises the following steps: pre-processing the user input data, extracting the key information from the input data; establishing the social network graph model; setting the parameter; executing the community discovery algorithm; outputting the discovery result and listing the criminal suspects; compared with the prior art, the method and the system have the beneficial effects as follows: the potential associated criminal suspect can be found from the social circle of the given criminal suspect, the method is good in performance and fast in system operation, by considering the requirement of the real scenes, good expansibility can be achieved for analyzing in the range appointed by the user.

Self-asserted socio-demographic attributes of individuals' identities are verified using social network analysis and other means. Through these processes, parties to a transaction or interaction arc provided a measure of confidence about another party's self-asserted socio-demographic attributes, such as age, gender, marital status, etc., in order to assist in determining whether or not to pursue the transaction or interaction. The measure of confidence may be provided as a quantitative “score” indicative of the likelihood the user's self-asserted attribute is actually true. The quantitative score is derived by analyzing a web of trust in which the user is embedded.

The present disclosure describes an apparatus and methods for performing data analysis, in particular network analysis. Specifically it provides a framework and user interface whereby raw data may be imported into a data model with entities, attributes, and relationships between the entities and multiple data analysis algorithms may be pipelined into a larger data analysis model and applied to the data model. Offline and online data visualization and data exploration techniques, may be applied to the results of the data analysis enabling real-time and near real-time interactive online analysis. Extensibility models and market transaction models are also provided.

Disclosed are methods and systems for enabling analysis of communication content while preserving confidentiality. In one embodiment, communication content is processed to increase the similarity of superficially dissimilar instances of communication content and/or to increase the distinctiveness of superficially similar instances of communications content. In this embodiment at least part of the processed communication content is hashed to obscure the actual communication content. In one embodiment, social network analysis is performed on the communication content after hashing, and visualization of the social network analysis includes thread graphs and/or circular graphs.

Healthcare fraud detection is accomplished by mining social relationships and analyzing their patterns based on network data structures. Social networks are constructed which depict referral patterns (from health insurance claim information) and associations (from publicly available connection data) to analyze referral patterns and detect possible fraud, abuse and unnecessary overuse. The fraud and abuse management system supports the various aspects of fraud investigation and management, including prevention, investigation, detection and settlement. Using a unique combination of data mining capabilities and graphical reporting tools, the system can identify potentially fraudulent and abusive behavior before a claim is paid or, retrospectively, analyze providers' past behaviors to flag suspicious patterns.

The present invention is directed to a system, method and program product for providing teleportation suggestions to avatars in a virtual universe. A service is provided that determines the set of teleportation suggestions for the avatar based on at least one of: an inventory analysis of the avatar, a historical teleportation analysis of the avatar, a motion analysis of the avatar, a social network analysis of the avatar, or a ratings analysis of locations within the virtual universe.

The invention provides a method and system for recommending friends in a social network site (SNS) community. The method comprises the following steps: acquiring behavior information of a user; generating a friend recommending list according to the behavior information combined with community information; and recommending the friend recommending list to the user. By using the method and system for recommending the friends in the SNS community, the probability for the user to find the friends can be greatly improved; and simultaneously, the method and the system can be initiatively provided to the user when the user logs in the community, which facilitates the user to select people possibly known with each other or desired to be known by the user, thereby enhancing the viscosity of the community so as to pull the vitality of friend relation in the community.

Example embodiments of systems and methods for network pattern matching provide the ability to match hidden networks from noisy data sources using probabilistic multi-attribute graph matching analysis. The algorithms may map roles and patterns to observed entities. The outcome is a set of plausible network models. The pattern-matching methodology of these systems and methods may enable the solution of three challenges associated with social network analysis, namely network size and complexity, uncertain and incomplete data, and dynamic network structure.

The invention belongs to the field of data mining, aims to solve the problem of excavating core authors in an academic field and intelligently extracting related information of the authors, and provides an improved academic core author excavation and information extraction method and system based on a core node discovery algorithm in the social network analysis technology. The method combines the vertical search technology, the social network analysis technology and the text analysis technology, and can find the core authors or groups of the academic field in the mass of information to further obtain related information of the authors. The method uses the vertical search technology to collect open source literature data, uses the bibliometric technology and the complex network analysis technology to analyze the importance of a variety of social entities in the data, and utilizes a community discovery algorithm to perform entity clustering based on the closeness degree of relationships between entities to find out an academic community. Users can find the core authors or an institution according to an entity importance ranking, and find the leadership team according to published articles amount distribution of cooperative groups.

The present invention relates to the social network analysis field, in particular to a microblog popularity degree prediction method based on a user and microblog theme and a microblog popularity degree prediction system based on the user and microblog theme. The method comprises the steps of obtaining the microblog data and the user data in a preset time period, obtaining the user attribute characteristics and the microblog theme characteristics according to the microblog data and the user data, carrying out the normalization processing on the user attribute characteristics, carrying out the user clustering on the processed user characteristics, and obtaining the user class information according to a clustering result; according to the microblog theme characteristics and the user class information, obtaining a forwarding characteristic of the user clustering under a microblog theme, and calculating a weight coefficient under the microblog theme of the user clustering; according to the microblog theme characteristics, the user attribute characteristics and the weight coefficient, constructing a microblog popularity degree prediction model, and predicting the microblog popularity degree according to the microblog popularity degree prediction model.