System for synchronizing nodes on a network
A system of computers on a wide area network establishes connections between nodes on the basis of their multidimensional similarity at a particular point in time in a certain setting, such as a social learning network, and sends information of value to those nodes. Dimensions in the definition of similarity include a plurality of attributes in time and community space. Examples of such dimensions and attributes may include a position in a learning community's project cycle, titles of readings and projects, the genre or subject matter under consideration, age, grade, or level of the participants, and language. The network's nodes are represented as a tensor field and are searched efficiently and adaptively through a variety of multidimensional data structures and mechanisms. The system includes a master clock that can transform a participant's time coordinates on the network, such as a social learning network, into Universal Time, and the synchronizer coordinates the position of each participant.
1. A method for determining node similarity by calculating, within a connected computer network, a comparative distance for each pair of a plurality of nodes on the connected computer network, wherein each node comprises a plurality of attributes and each attribute includes a value, comprising the steps of:
a. having a computer identify a plurality of attributes corresponding to nodes on the connected computer network;
b. having a computer perform a comparison of a first attribute corresponding to a first node on the connected computer network to a first attribute corresponding to a second node on the connected computer network to determine a relative distance between the first attribute of the first node on the connected computer network and the first attribute of the second node on the connected computer network, wherein the comparison comprises
i. creating a column matrix of pairwise distances between the first attribute of the first node and the first attribute of the second node on the connected computer network;
ii. comparing the relative magnitudes of the eigenvalues of the columns of the matrix and determining the estimated positions of the first node and the second node based on which columns of the matrix contribute most to the estimation of the positions of the first node and the second node;
c. having the computer assess the similarity between the first and second nodes on the connected computer network by calculating the comparative distance based on a plurality of the estimated positions determined at step (b); and
d. having the computer identify one or more additional nodes on the connected computer network based on the assessed similarity between the first node on the connected computer network and the second node on the connected computer network.
2. The method of determining node similarity of claim 1 further comprising the step of scaling the pairwise distances between the first attribute of the first node and the first attribute of the second node.
3. The method of determining node similarity of claim 1 wherein the value of each attribute is dynamic and capable of constantly and continuously changing.
4. The method of determining node similarity of claim 1 wherein the first node on the connected computer network and the second node on the connected computer network are part of a subset of the plurality of nodes on the connected computer network, wherein the subset is identified based on at least one of the plurality of attributes.
5. The method of determining node similarity of claim 1 wherein the nodes are capable of displaying information.
6. The method of determining node similarity of claim 1 wherein the method further comprises the step of self-updating the assessing of the similarity between the first and second nodes on the connected computer network.