Abstract: The disclosed embodiments provide a system for performing inference. During operation, the system obtains a graph containing nodes representing members of an online system, edges between pairs of nodes, and edge scores representing confidences in a type of relationship between the pairs of nodes. Next, the system performs a set of iterations that propagate a label for the type of relationship from a first subset of edges to remaining edges in the graph, with each iteration updating a probability of the label for an edge between a pair of nodes based on a subset of edge scores for a second subset of edges connected to one or both nodes in the pair and probabilities of the label for the second subset of edges. The system then performs one or more tasks in the online system based on the probability of the label for the edge.

Abstract: Techniques for using machine learning to leverage deep segment embeddings are provided. In one technique, a set of training data is processed using one or more machine learning techniques to train a neural network and learn an embedding for each segment of multiple segments. In response to receiving a request, multiple elements are identified, such as a source entity that is associated with the request, a source embedding for the source entity, a particular segment with which the source entity is associated, a segment embedding for the particular segment, and multiple target entities. For each target entity, a target embedding is identified and the target embedding, the source embedding, and the segment embedding are input into the neural network to generate output that is associated with the target entity. Based on the output, data about a subset of the target entities is presented on a computing device.

Abstract: Techniques for generating recommendations using a generalized linear mixed model with destination user personalization are disclosed herein. In some embodiments, a computer system generates corresponding scores for destination user candidates based on a generalized linear mixed model comprising a global model and a destination user model. The global model is a generalized linear model based on feature data of a source user and feature data of the destination user candidates, and the destination user model is a random effects model based on behavior data of the destination user candidates indicating whether the destination user candidates performed a destination user action in response to a source user action performed by reference source users similar to the source user. The computer system selects a subset of the destination user candidates for recommendation to the source user based on the scores of the subset of the destination user candidates.

Abstract: Technologies for scoring and ranking cohorts containing content items using a machine-learned model are provided. The disclosed techniques include a cross-cohort optimization system that stores, within memory, cohort definition criteria for each cohort of a plurality of cohorts. The optimization system, for a particular user, for each cohort, identifies a plurality of content items that belong to the specific cohort based upon the cohort definition criteria. Using a machine-learned model, the optimization system generates a score for the specific cohort with respect to the particular user's intentions. The optimization system generates a ranking for the plurality of cohorts based on the respective scores of each cohort. The optimization system causes the plurality of content items of each cohort to be displayed concurrently on a computing device of the particular user. Display order for the plurality of cohorts is based on the ranking determined for the plurality of cohorts.

Abstract: A cohort service is configured to address the technical problem of providing recommendations to a member of an online connection network system in a manner that alleviates potentially excessive cognitive load associated with presenting recommended entities indiscriminately as a scrollable list. The cohort service is configured to visually surface recommended relevant entities already grouped as cohorts. A cohort is a grouping of entities based on one or more common attributes, such as, e.g., same school, same company, etc. The cohort service is designed to group recommendation results into cohorts at the server side, which increases the liquidity and the relevance of the recommended entities so that the already grouped recommendations can be sent to the client computer system for presentation to a viewer.

Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein are directed to Future Connection Engine that generates a select pairing of member accounts for a potential social network connection. The Future Connection Engine predicts, according to the prediction model, a first number of subsequent social network connections for a first member account in the select pairing that will occur after establishing the potential social network connection and a second number of subsequent social network connections for a second member account in the select pairing that will occur after establishing the potential social network connection. The Future Connection Engine generates connection recommendations for display to the select pairing based on whether the first and/or the second number of subsequent social network connections satisfies a threshold.

Abstract: In an embodiment, the disclosed technologies include computing a score for a node pair including first and second nodes of a digital connection graph; where nodes of the digital connection graph represent members of an online system; where the online system uses the digital connection graph to determine a runtime decision related to a member represented by the first node; where the score indicates a predicted likelihood of interaction, during a time interval, after a digital connection between the first and second nodes of the node pair; where the predicted likelihood of interaction is determined by comparing a set of statistics computed for the node pair to a digital model; where the digital model has been created using data extracted from post-connection interactions in the online system between members whose nodes are connected in the digital connection graph; causing the score to modify the runtime decision.

Abstract: The present invention relates to a nanocarrier peptide sequence (Sequence Id no. KXPXXXXA/V/GXGNXX; wherein X is selected from amino acid R, K, A or H. The present invention also relates to the method for cellular delivery, comprising the steps of: complexation of a peptide nanocarrier sequence: XPXXXXA/V/GXGNXX; where X is selected from amino acid R, K, A and H having SEQ ID NO 6 with a macromolecule to obtain a complex; and administering the complex to a targeted mammalian or plant cell or tissue.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system retrieves, from a nearline data store, one or more updates representing recent activity for a member of an online network. Next, the system performs one or more queries using data in the updates to identify a set of candidates for recommending to the member. The system then applies one or more machine learning models to features for the set of candidates to generate a ranking of the set of candidates and updates the ranking based on additional features for an additional set of candidates from an offline data store. Finally, the system outputs, to the member, at least a portion of the updated ranking as connection recommendations in the online network.

Abstract: Techniques for optimizing a guided edit process for editing a member profile page are described. According to various embodiments, profile edit task information associated with a member of an online social network service is accessed, the profile edit task information identifying one or more candidate profile edit tasks to be performed to update a member profile page of the member. Thereafter, if it is determined that the member recently completed a difficult profile edit task, a difficult candidate profile edit task is identified, and the member is prompted to perform the difficult candidate profile edit task. If it is determined that the member has not recently completed a difficult profile edit task, an easy candidate profile edit task is identified, and the member is prompted to perform the easy candidate profile edit task.

Abstract: Systems, devices, media, and methods are presented for identifying and facilitating connections in a social network. The systems and methods identify a plurality of unconnected members having a number of associations below a first threshold and identify a specified member with a number of associations above a second threshold. The systems and methods determine one or more connected members having an attenuated association with the specified member and identify a set of presentation positions within a graphical user interface. The systems and methods determine a presentation trigger selecting a connection type of a set of members to be presented to the specified member. Based on the presentation trigger, the systems and methods select an unconnected member for presentation within the set of members and cause presentation of the unconnected member within a presentation position.

Abstract: This disclosure relates to systems and methods for identifying members that increase engagement at an online social network. In one example, a method includes retrieving network connectivity and member interaction data for members of an online social networking service that includes a plurality of explicit social networks, building statistical correlations between properties of the respective explicit social networks and interactions between members of the respective explicit social networks, and ranking a set of potential new members for one of the explicit social networks according to the statistical correlations and a statistical likelihood that the new members will increase member interactions with the explicit social network.

Abstract: A system, device, and method may include a network interface device configured to be communicatively coupled to a user interface. An electronic data storage may be configured to store social network data related to users having accessed a social network content item A processor, coupled to the network interface device and the electronic data storage, may be configured to identify a relationship among at least some of the users, determine a relevance of the relationship to a member of the social network associated with the social network content item based, at least in part, on a social network profile of the member and social network profiles of the users, and cause the network interface device to display, on the user interface, information related to the relationship on the user interface based, at least in part, on the relevance.

Abstract: A method and system to suggest keywords to a social network member is described. A suggested keywords system, in one example embodiment, examines phrases that appear in profiles maintained by the on-line social networking system that are similar to the target profile and identifies those words and phrases that are most prominent in these profiles, utilizing a graph-based approach. These most prominent words and phrases may be presented to the target member as suggested keywords to be included in the member's professional summary.

Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein are directed to Future Connection Engine that generates a select pairing of member accounts for a potential social network connection. The Future Connection Engine predicts, according to the prediction model, a first number of subsequent social network connections for a first member account in the select pairing that will occur after establishing the potential social network connection and a second number of subsequent social network connections for a second member account in the select pairing that will occur after establishing the potential social network connection. The Future Connection Engine generates connection recommendations for display to the select pairing based on whether the first and/or the second number of subsequent social network connections satisfies a threshold.

Abstract: A suggested keywords system is configured for identifying phrases, which are most relevant to experience and expertise of a professional network member, and which the member may be interested in weaving into their profile summary. The suggested keywords system generates a model, for each phrase, that calculates probability of that phrase being present in a profile that is characterized by the absence of certain attributes and by the presence of certain attributes. Based on the model, the suggested keywords system calculates a ranking value for the phrase for a particular target profile. The phrases with the higher rank are considered to be more relevant in describing professional background of the target member. A certain number of phrases that have the highest ranking are presented to the member as suggested keywords to be included in their professional summary.

Abstract: The disclosed embodiments provide a system for facilitating interaction within a social network. During operation, the system obtains a set of features associated with two members of a social network, wherein the features comprise a member feature and an activity feature. Next, the system analyzes the features to predict an effect of a potential interaction between the two members on subsequent interactions between the two members in the social network. The system then uses the predicted effect to generate output for modulating the subsequent interactions in the social network.

Abstract: The disclosed embodiments provide a system for facilitating interaction within a social network. During operation, the system identifies a first member of a social network with a first activity level that is lower than a threshold. Next, the system uses the first activity level to boost a score associated with recommending an interaction with the first member to a second member of the social network. The system then uses the boosted score to generate output for modulating subsequent interactions in the social network.

Abstract: This disclosure relates to systems and methods for identifying members that increase engagement at an online social network. In one example, a method includes retrieving network connectivity and member interaction data for members of an online social networking service that includes a plurality of explicit social networks, building statistical correlations between properties of the respective explicit social networks and interactions between members of the respective explicit social networks, and ranking a set of potential new members for one of the explicit social networks according to the statistical correlations and a statistical likelihood that the new members will increase member interactions with the explicit social network.

Abstract: A machine may be configured to blend connection recommendation streams. For example, the machine, based on a member identifier of a member of a SNS, accesses a list of other members of the SNS and a list of guests (e.g., non-members). The machine identifies a member probability value representing a likelihood of the member inviting another member to connect via the social graph of the member, and identifies a guest probability value representing a likelihood of the member inviting a guest to connect via the social graph. The machine generates a blended list of other members and guests based on the member probability values, the guest probability values, and a coefficient value selected to control a presence of a type of connections in the blended list. The machine generates recommendations for the member to invite people included in the blended list to connect with the member via the social graph.