I’m a heavy LinkedIn user, and like many of my ilk — recruiters, marketers, job seekers — I’m quick to jump to search for people-finding. That’s because exploring a social network, connection-by-connection, is beyond tedious. All but poorly-connected users will have millions in their networks, within 2 or 3 links, and manual exploration simply doesn’t scale. (Cutting connections isn’t the answer; it destroys a network’s value, which grows steeply with the number of participants according to the network effect.) Further, you can’t explore what you can’t see. Because many individuals hide their connection lists, a connection-surfer won’t see entire branches of her network. So I — and you — use search.
Unfortunately, for all the professional social platform’s data-science mojo, elements of LinkedIn search are disappointingly weak. I’ll illustrate those elements and describe how LinkedIn can better help users discover business value in their connections.
The State of Search on LinkedIn
The search user interface gold standard is a simple text-entry box, and LinkedIn obliges, with the added possibility of using a drop-down to focus search on People, Jobs, Companies, Groups, Universities, Articles, and Inbox. (That latter label is strange, given that “Inbox” search searches all your messages. And by the way, I’m using the Web interface although my points apply even more to the mobile app, which, as is typical, offers reduced capabilities.) Or click on Advanced and get this window:
And here we see the weakness. LinkedIn offers People Search, fine if you’re looking for individuals, identified by particular attributes, by particular facet values. LinkedIn does not offer search that exploits its essential identity; LinkedIn does not offer Network Search.
Your Networks, Via Your Connections
I’ll explain what I mean by “network search” by showing that’s the data’s there, in LinkedIn. Consider a LinkedIn search results screen, mine:
(Actually, this screen represents a null search; it’s the start of a listing of my connections.)
We have faceted navigation — the ability to filter on values of characteristics including distance of connection (1st, 2nd, Group) and physical location — and we can see network attributes, in particular, the number of connections I share with each individual.
I don’t have a clue how LinkedIn did the ranking here, despite the (fairly opaque) explanation the company provides. For instance, of the seven individuals you see displayed, I’ve met only two in person, and only one of them more than once, Howard Dresner. Howard and I share 138 connections; that our networks so significantly overlap is a strong indicator of profile similarity, his and mine. Yet the top-of-the-list individual and I share only eight connections, I’ve never met him in person, and I’ve interacted with his company on only one occasion. How did he get to be top ranked for me?
We’re in a damned-if-you-do, damned-if-you-don’t situation. If the answer to this apparently anomalous ranking is that it’s an unordered list, not a ranking, then the question is, Why not? And if the answer is that LinkedIn is applying a ranking algorithm that makes sense only to the company’s engineers but not to a user like me, then the questions are, WTF? and Why can’t I have the ranking my way?
I want to be able to order hits by descending number of shared connections. I can’t, and that’s because, again, LinkedIn offers only People Search when I also want Network Search.
It’s a Network, Not Just a Set of Nodes
LinkedIn hosts social networks. In social networks, the number of short connections between two individuals in the graph is a good indicator of similarity, of shared interests. But I don’t want only to rank search hits by number of connections. I want to use other interesting network properties in my searches. LinkedIn gets the concepts; they just don’t let me use them. What concepts? Clusters, or subnets if you will. Take a look at my LinkedIn InMap:
Each colored node-set represents individuals who share a certain affinity with me, based on network structure. LinkedIn says, “We use information about how people in your network are connected to you and each other to create your personalized map. Groups like colleagues, people you went at school with, or friends are separated into color-coded clusters, as people within these groups are also interconnected with each other.”
Network Search would allow me to limit searches to members of particular clusters and their connections. In my case, that would mean I could restrict search to my BI & data warehousing connections (the azure cluster toward the top of my InMap) or my market research connections (pea green toward the lower right) or my European information retrieval and text analytics connections (reddish, lower left), in order to improve results relevance. What I’m asking is for LinkedIn to support a new, dynamic search facet.
Relevance is key, and leads me to my final topic, recommendations, which I’d characterize as a passive form of search.
When Will They Ever Learn?
I won’t accept a connection request from a recruiter or sales person whom I haven’t actually interacted with, nor from a student who doesn’t have any non-academic accomplishments that are professionally relevant to me, nor a request from a non-professional contact such as a community member. I follow this rule because I use LinkedIn almost exclusively for professional networking, and mixing in these (literal) outliers would make it much more difficult for me to correctly target my outreach efforts.
So who does LinkedIn recommend, as People You May Know? Check ’em out, in the image at the right. Nothing personal, but they are not people I will be connecting to, and LinkedIn should have known that.
One of them works professionally in an area related to mine, although not closely. A second shares one connection with me and lives near my community, with no professional connection however, and a third is three links away from me with our only similarity being that she also lives near my community.
I do, occasionally take LinkedIn connection recommendations, but never for people like these, and I have ignored many connection requests from people similar to them. I have done many, many LinkedIn searches, on terms such as “sentiment analysis” and “customer experience,” but never on terms that would turn up these people.
If LinkedIn did a bit of behavior mining — my search, profile viewing, and connection requests initiated or accepted — the platform’s People You May Know recommendations would be far more accurate. Machine learning could do the job; not an small task given the number and diversity of LinkedIn users, but then you don’t have to mine every user’s actions in order to come up with predictive recommendation models that would surely out-perform what’s in place now.
My Search for Relevance
I have high expectations for LinkedIn. I know that the company has great data-mining and information-retrieval capabilities, but also I pay them quite a bit. (I’ve been a Premium user on and off; I downgrade, to save money, for stretches when I’m not going to be using the platform extensively.)
So consider this article a challenge from a faithful user. Help me do my job better. Factor network characteristics and user behaviors into searches, and help me in my search for relevance.