An obvious approach is to categorize a person's subscriptions. Let's say that among the subscriptions of the user Ivan we found two autobloggers and three accounts with funny pictures, the rest of the profiles are not thematic (we will assume that these are friends, relatives, colleagues, etc.). Hence we conclude: Ivan is a cheerful person, he is fond of cars. Profit. All we need is a “blogger + content topic” match, but it's not that simple here. Buying cheap Instagram followers can be an optimal way to improve your page can be an optimal way to improve your page
Who is a blogger? A blogger on Instagram is no different from a regular account (and in general, this is something of a state of mind). Ideally, we are interested in thematic accounts with a large share of a live audience, while we would not want to just cut off by the number of subscribers, we can miss something.
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Where can I get a good taxonomy? If you come up with categories on your own, then surely something interesting will get lost in the “different”, and when everything is ready, it turns out that the categories are too broad. We want to get a natural split, and therefore we will cluster bloggers.
Can one blogger belong to more than one category? And how! Very often he is both a Swiss, and a reaper, and a tune-player at the same time (and also travel, lifestyle, wife and mother).
So, we need a dataset with a very large number of bloggers and a clustering algorithm with overlapping clusters at the output.
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We are approached by clients who want to place their ads with influencers on Instagram in the most effective way. Sometimes there is a list of bloggers, and then we solve the optimization problem - to reach more people for less money, and sometimes there is no list and we need to find relevant bloggers. This time we looked for the profiles of ophthalmologists.
- We select 3-7 suitable profiles by hand (“core of the community”(
- Using the intersection of subscribers, we identify people who are interested in the topic ("enthusiastic")
- We are looking for new doctors in the subscriptions of "enthusiastic" people
- If necessary, repeat steps 1-3 (adding new doctors to the "core" or changing it)
Choice and description of the ibstagram algorithm
Excellent! The data has been prepared, now you need to choose a tool and an algorithm that will cope with such a large graph, preferably in adequate time. buy instagram likes uk can expand your page .
Our attention was drawn to the AGMfit algorithm (AGM - Affilated Graph Model), and eventually BIGCLAM (Cluster Affiliation Model for Big Networks) was used. They differ only in that in the first case, the optimization problem is solved combinatorially, which makes it less scalable, and the second just allows you to input graphs like ours.
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The basic and rather intuitive idea: the more common communities two nodes have, the more likely a connection between these two nodes is. Both algorithms are based on the Affilated Graph Model, so let's dwell on it in more detail:
Suppose we have a bipartite graph, in which the round vertices are communities (and), and the square vertices are social network users, and each person () belongs to different communities with certain weights (and). The more weight, the more likely it is thatthemember is connected (familiar) with other members of the community.