TECHNICAL FIELD
The present disclosure relates to information routing in a distributed network. More particularly  the embodiments of the disclosure relate to a method for routing information in a distributed network based on optimal influence.
BACKGROUND
A distributed network is a network structure in which individual nodes are connected to one or more nodes via links. One important emerging class of problems in the distributed network involves relying on expertise of individual nodes to find responses to specific information needs. Within this context  one can assume that any two nodes in the distributed network are connected by one or more paths. Moreover  expertise tends to be distributed throughout the distributed network such that  for any information need  there are one or more nodes within the network for which  partial or full answer to the query is easily at-hand. Thus  in general  there exists  for most queries  one or more nodes at varying distances from the query originator node  which has full or partial answer to the query. When an information request reaches a node  it is a choice available to the node  whether or not to take any action on the information request. The problem  however  is that while a path to a query""s answer node(s) may exist within the distributed network  the optimal influential path  which will influence a node to take a set of desired action when triggered by an information request is typically hard to identify.
Hence  there exists a need for a method to identify a most optimal influential path from a source node to the destination node  so that desired action is taken by the destination node.
BRIEF DESCRIPTION OF THE DRAWINGS
The novel features and characteristics of the disclosure are set forth in the appended claims. The embodiments of the disclosure itself  however  as well as a preferred mode of use  further objectives and advantages thereof  will best be understood by reference to the following detailed description of an illustrative embodiment when read in conjunction with the accompanying drawings. One or more embodiments are now described  by way of example only  with reference to the accompanying drawings in which:
Fig.1 illustrates a process of influencing a node in accordance with the present disclosure.
Fig.2 illustrates process of influence relationship between the nodes.
Fig. 3 illustrates process of influencing a node by more than one node in a distribution network.
Fig.4 illustrates process of context based action.
Fig.5 illustrates a system for routing information based on computing optimal influence in accordance with the present disclosure.
Fig.6 illustrates an optimal destination node computing method in accordance with the present disclosure.
Fig.7 illustrates an optimal next-hop computing method in accordance with the present disclosure.
Fig.8 illustrates process cycle for computing optimal influential path that routes information request to destination in order to procure a desired response in a distributed network.
Fig.9 shows a flowchart illustrating a process for taking a message from source node to destination node through the optimal influential path  so that desired end conditions are achieved in accordance with an embodiment of the present disclosure.
Fig.10 illustrates process of computing a feedback influence score.
Fig.11 illustrates process of computing a feedback score for a path traversed between nodes of a distribution network.
The figures depict embodiments of the disclosure for purposes of illustration only. One skilled in the art will readily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION
In an embodiment  the present disclosure provides a method to determine a path between a source node and a destination node in the distribution network which has the optimal influence. Influence is the ability to drive action from another node in the distribution network. For example  a hiring manager of a company posts a job posting and shares it with a recruiter of the company. The recruiter forwards the job posting to potential candidates. The influence exerted by the recruiter on a potential candidate depends on the influence power of the recruiter and their relationship. In many cases  the potential candidate ignores the message from the recruiter. An alternate path to route messages from the hiring manager to the potential candidate is to find a set of influential paths starting from the hiring manager to reach the potential candidate or reach the potential candidate through one of the candidate’s own friends who has more influence on the candidate than an unknown recruiter.
Fig.1 illustrates a process of influencing a node in a distribution network. As an example  Node 1 and Node T are the two nodes in the distribution network. Node 1 has a message/information request. When Node T receives the request  it can take one or more actions. Node 1 predicts the set of actions that will be taken by the Node T. Node 1 also observes the actual action taken by the Node T in response to the message request. When Node 1 sends a message to Node T  it exerts an influence push on Node-T in order for Node-T to take one or more actions. The one or more actions that can be taken by Node T are  responding to the message from Node 1  sharing the message  forwarding the message   giving direction (routing) information for further routing of the messages or a combination of them. In an embodiment  Node T can block the messages from Node 1  mark the received message as noise or even ignore the message.
Fig.2 illustrates process of influence relationship between the nodes. The influence relationship between the nodes of the distribution network can be symmetric or asymmetric. As an example  Node S  Node 1  Node 2 and Node 3 are the four nodes in the distribution network. Node S influences Node 1 based on which Node 1 performs one or more actions requested by Node S. Based on these actions the influence score for the relationship between Node S and Node 1 is A. Similarly  Node S influences node 2 for which the assigned influence score is B and Node S influences Node 3 for which the assigned influence score is C. similarly  Node 1 influences Node S based on which Node S performs one or more actions. Based on these actions the influence score for the relationship between Node 1 and Node S is D. Similarly  Node 2 influences node S for which the assigned influence score is E and Node 3 influences node S for which the assigned influence score is F. The influence score between the two nodes is stored in the Influence database (DB).
Fig. 3 illustrates process of influencing a node by more than one node in a distribution network. As an example  Node-1  Node-2 and Node T are the three nodes in the distribution network. Node 1 and Node-2 exerts an influence push on Node-T in order for Node-T to take a desired action. When more than one node is exerting the influential push  the probability of Node-T taking desired action increases.
Fig.4 illustrates process of context based action. As an example  the distribution network consists of Node S which is a source node  Node D  a destination node and other nodes such as Node 1  Node 2  Node 3  Node 4  Node 5 and Node 6. A message from the source node S should be routed to the destination node D. For routing the message from the source node S to the destination node D there are three different paths  Path 1  Path2 and Path 3. Path 1 directly connects Node S to Node D. Path 2 is from Node S-Node 1- Node 2-Node 3-Node 4-Node D. Path 3 is from Node S-Node 5-Node 6-Node D. In Path 1  when Node S is forwarding the message to the next hop  in this case Node D  the expected highest priority desired action is that Node D will respond to the message from the next hop Node D first. The highest impact of action for Path 1 is responding to the message.
For Path 2 and Path 3  the highest impact of action when Node S is sharing the messages with its next hop (Node 1 and Node 2 respectively) i.e in Path 2  Node 1 receives the message from Node S and shares the message or forwards the message to the next node. Similarly in Path 3  Node 5 receives the message form node S and shares the message with Node 6 for further routing of the messages. In Path 1 and Path 2  the lowest impact of the action is responding to the message. Depending on the path and the next hop the desired hierarchy of desired action will be different.
Fig.5 illustrates a system for routing information in a distributed network based on computing optimal influence. The system comprises of a parameter database (DB)  engagement models  nodes database DB  influence score DB and influence computing methods block. When a node gets a new message and a goal in a distributed network  the influence computing methods block extracts set of parameter set  which contains all the parameters required for routing the message from current node to the destination node. The parameter set is present in the parameter DB. For example  given a job description  the goal is to find the destination nodes i.e to find the people who will apply for the given job. The job parameters like  job description  profile of the company  profile of the hiring manager  description about ideal candidates etc. are present in the parameters DB. Given the current knowledge about the message  the goal and the parameter set  the influence computing methods block finds influential scores between various participating nodes and stores the score in influence score DB.
Fig. 6 illustrates an optimal destination node computing method in accordance with the present disclosure. The method comprises computing a set of optimal destination nodes which can potentially responds to the message partially or fully. The optimal destination nodes can be ordered rank wise or can be grouped into clusters.
Fig. 7 illustrates a method for computing a set of optimal next-hop nodes. Given a set of destination nodes  starting from the current node in the direction of the destination nodes  the method computes the next hop nodes. The next hop nodes optimized the influence for the path from current node to the destination. The identified optimal influential next hop nodes can be ordered rank wise or clustered. The message from the source node is routed to the identified next hop node(s). For the routed path  the system evaluates the engagement results. The identified next hop node(s) performs certain actions and based on the actions taken by the next hop node  the system updates the parameter set. The influence computing methods block also re-computes the influence score and updates the influence score in the influence score DB.
Fig.8 illustrates process cycle for routing information in a distributed network based on computing optimal influence. For a given message and a goal  firstly the optimal destinations nodes are found. Given the optimal destination nodes  the system finds optimal influential next hop nodes. The optimal influential nodes perform one or more actions which are evaluated. Based on the actions the system updates the parameters and the influence score. This process repeats till responses to the message have been found.
Fig.9 shows a flowchart illustrating a process for taking a message from source node to destination node through the optimal influential path  so that desired end conditions are achieved in accordance with an embodiment of the present disclosure. When a node gets a new message and a goal  the node extracts a set of parameter set  which contains all the parameters required for routing the message from the current node to the destination node. Given the current knowledge about the message  the goal and the parameter set  the system finds a set of optimal destination nodes who could respond to the message. Given the optimal destination nodes  the system finds the optimal influential next hop nodes starting from the current node in the direction of the destination nodes. The message is routed to the identified next hop node(s). For the routed path the system evaluates the engagement results. Based on the type of actions taken by the next hop node(s)  the system updates the parameter set and influence DB. The system checks whether the end conditions for the message has been reached. If the end conditions are not yet achieved  then the system re-computes the destination nodes  given the updated parameter set and the process repeats from the current node till the goal is reached.
Fig.10 illustrates process of computing a feedback influence score. As an example  Node S and Node N are the two nodes in the distribution network. The message from Node S is forwarded to Node N. Node S influences node N in order for node N to perform one or more actions. The system uses the predicted influence score to route the message forward to the Node N. When the message reaches the Node N  the system evaluates the actual action taken by the next-hop node. If there is a difference between the predicted action and the actual observed action taken by the Node N  the system computes a feedback influence score between Node S and Node N  which is used to accurately predict the influence of Node S on Node N for future interactions. If the predicted action and the observed action match  the system updates the influence score between Node S and Node N by a delta  to reflect the improved influence of Node S on Node N.
Fig.11 illustrates process of computing a feedback score for a path traversed between nodes of a distribution network. The distributed network for example includes one source Node S  two destination nodes  Node D and the intermediary nodes  Node I. The system uses the predicted influence score to route the message forward to the next hop node. Once the whole path has been traversed and the end condition has been achieved  the system computes a path feedback score  which is a metric between predicted metric and the actual observed metric. This path quality feedback score is used to update the influence scores of all the nodes which participated in that particular path.
Finally  the language used in the specification has been principally selected for readability and instructional purposes  and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description  but rather by any claims that issue on an application based here on. Accordingly  the disclosure of the embodiments of the invention is intended to be illustrative  but not limiting  of the scope of the invention  which is set forth in the following claims.
With respect to the use of substantially any plural and/or singular terms herein  those having skill in the art can translate from the plural to the singular and/or from the singular to the plural as is appropriate to the context and/or application. The various singular/plural permutations may be expressly set forth herein for sake of clarity.
While various aspects and embodiments have been disclosed herein  other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting  with the true scope and spirit being indicated by the following claims.